Interactions between proteins play a key role in many cellular processes.Studying protein-protein interactions that share similar interaction interfacesmay shed light on their evolution and could be helpful in elucidating themechanisms behind stability and dynamics of the protein complexes. When twocomplexes share structurally similar subunits, the similarity of the interactioninterfaces can be found through a structural superposition of the subunits.However, an accurate detection of similarity between the protein complexescontaining subunits of unrelated structure remains an open problem.Here, we present an alignment-free machine learning approach to measure interfacesimilarity. The approach relies on the feature-based representation of proteininterfaces and does not depend on the superposition of the interacting subunitpairs. Specifically, we develop an SVM classifier of similar and dissimilarinterfaces and derive a feature-based interface similarity measure. Next, thesimilarity measure is applied to a set of 2,806×2,806 binary complex pairsto build a hierarchical classification of protein-protein interactions. Finally,we explore case studies of similar interfaces from each level of the hierarchy,considering cases when the subunits forming interactions are either homologousor structurally unrelated. The analysis has suggested that the positions ofcharged residues in the homologous interfaces are not necessarily conserved andmay exhibit more complex conservation patterns. Interactions between proteins form protein complexes and underlie many cellularprocesses L_RMSD) measure, which is defined as a RMSD value between theback-bones of the smaller subunits (ligands), once the corresponding larger subunits(receptors) are superimposed Several approaches to quantify the interface similarity have been proposed to date.Some approaches rely on a superposition of the entire structures of the interactingproteins Interaction similarity is also used to cluster protein-protein interactions The goal of this work is finding an accurate alignment-free interface similaritymeasure and demonstrating its advantages and applicability. First, we introduce anaccurate structure-based interface similarity measure that is used to generate atraining set of similar and dissimilar interfaces. We then describe a feature-basedinterface similarity measure by employing a supervised learning approach, which istrained on the known structures of protein-protein interfaces. Furthermore, we applythe feature-based similarity measure to develop (i) a proof-of-concept hierarchicalclassification of protein interactions, and (ii) a data structure for efficientsearch and retrieval of similar interfaces. The classification can be useful in theevolutionary studies of protein interactions, as illustrated by our case studyanalysis.iiRMSD andsiRMSD. Second, we apply the more accurate of the two measuresto a non-redundant set of protein interfaces to determine reliable positive andnegative training sets for our feature-based measure. Third, we use the training setto obtain two Support Vector Machine (SVM) models, resulting in two feature-basedinterface similarity measures. Finally, we employ one of the new feature-basedsimilarity measures to (i) define a structure-based hierarchical classification ofprotein interaction interfaces on a large scale, and (ii) design a data structurefor the interface search and retrieval problem.Our methods are organized as follows. First, we define and compare twostructure-based interface similarity measures, protein-proteininteraction, protein binding site, andprotein interaction interface since these concepts will beused throughout the paper. A protein-protein interaction is defined as a triple, where S1 andS2 are the two interacting subunits (eitherproteins or protein domains), and O is their relativeorientation. A residue 1r of one subunit is incontact with residue r2 of another subunit ifr1 has at least one atom within 6 Å of anatom of r2. The set of all residues from one subunitthat are in contact with any residues of another subunit constitutes a proteinbinding site. For a protein-protein interaction, its interaction interface isdefined by a triple , whereB1 and B2 are thebinding sites of the interacting subunits, and C is a set ofall pairs of residues that are in contact.We first formally define the concepts of a homologous if a subunit inthe first interaction shares homology with a subunit in the second interaction,and the remaining two subunits also share homology between each other. Twoprotein-protein interactions that share similar interfaces are calledcommon-partner analogous if a subunit in the firstinteraction shares homology with a subunit in the second interaction, while theremaining two subunits are structurally unrelated. Finally, two protein-proteininteractions that share similar interfaces are called analogousif both subunits in the first interaction are structurally unrelated to subunitsin the second interaction. The protein interfaces formed by interactions of thethree types are called homologous, common-partner analogous, and analogous,correspondingly.We next introduce three types of similar interaction interfaces based on theprotein-protein interactions they mediate. Two protein-protein interactions thatshare similar interfaces are called To train a feature-based similarity measure, one needs to generate two reliabletraining sets of similar (positive training set) and dissimilar (negativetraining set) interfaces. This is done by employing a structure-based similaritymeasure, a commonly used approach to compare homologous interfaces or interfacesformed by the same subunits iiRMSD), is defined by superposing overall structures ofthe interacting subunits, similar to L_RMSD measure, used inCAPRI docking assessment A1 and A2 andanother one between subunits B1 andB2, we calculate iiRMSD throughthe following steps:The first structure-based similarity measure, the interaction interface RMSD usingMultiProt software α-only RMSD between thecorresponding residues of the binding sites of iAand jB1. Structurally align subunit iA−jB:2. For each alignmentα-only RMSD between the corresponding residues of thebinding sites of remaining subunits2.1 Superpose the remaining two subunits according to the alignment; andcalculate Cα-only RMSD values2.2 Calculate an average of the two CiA andjB.3. Select the smallest of the calculated averages over four possiblesuperposition scenarios for siRMSD), is defined as the Cα-based RMSDbetween the corresponding residues of the structurally superposed interactioninterfaces. The structural superposition of interfaces is done using the sameMultiProt software iiRMSD,siRMSD is guided exclusively by the local structure of theinteraction interfaces, which can potentially lead to the incorrect detection ofsimilar interfaces, specifically when the interface structures are small.The second similarity measure, the superposed interface RMSD. To exclude a rare possibility of differentbinding modes that can occur for a pair of homologous or even identicalproteins, all pairs of obtained proteins are manually checked using subunitsequence similarity and symmetry information from 3D Complex.iiRMSD and siRMSD measures arecalculated and compared for all similar and dissimilar interface pairs in thedataset. Specifically, we use Bhattacharyya Coefficient based metric iiRMSD and siRMSDsimilarity distribution, using n = 50 binsapplying the same lower and upper thresholds (15 Å and 25 Å) as inthe first strategy. As a result, 723 dissimilar native-native interface pairswere selected  randomly selecting apair of interactions from the non-redundant set, such that all four subunitsforming the interactions belong to four different SCOP Superfamilies, (2)determining the selected .We willn1 pairs of similar andn2 pairs of dissimilar interfaces, where eachpair is represented as a vector of N numerical features,ix = ,the basic goal is to train a classifier that would classify a pair of theinterfaces as either similar or dissimilar. In its simplest form, the problemcan be viewed as finding a hyperplane that separates two classes of pointsmaximizing a margin defined by the closest to the hyperplane positive andnegative examples. The formalism can be expanded by introducing non-linearclassifiers defined through the kernel functions, For our approach we employ twowidely used non-linear kernel functions: the polynomial kernel,d is degree of the polynomial,and the radial basis function (RBF), lightSVM softwareTo determine whether two interaction interfaces are similar without the use ofstructural alignment, we train a feature-based similarity measure using aSupport Vector Machines (SVM) approach HPositive and 480 fromCPositive sets), and a negative set of 599pairs from NDNegative set. The second datasetincludes the same positive set, but the negative set combines 723 interfacepairs from NNNegative and 599 fromNDNegative sets. Second, for each interfacestructure, we calculate a 53-dimensional vector, which consists of featuresdescribing geometrical and physico-chemical characteristics of the interfaces.For the training procedure, all interface feature vectors are paired up,resulting in 106-dimensional feature vectors. Third, two SVM classifiers aretrained: classifier NDModel is based on the firstdataset and classifier NDNNModel is based on thesecond dataset. Fourth, for each model, a protein interface similarity measure1, I2)δ than similar pairs . Inaddition, the mean value difference between the similar and dissimilarinterfaces was larger when using the iiRMSD measure(Δμ is 4.7 for iiRMSD and 1.1 forsiRMSD). A more detailed analysis using BhattacharyyaCoefficient based metric iiRMSD values for the similar and dissimilarinterface pairs (BCd = 0.36),compared to the distributions of siRMSD values(BCd = 0.23). Thissuggests that iiRMSD may differentiate better between thesimilar and dissimilar interfaces than siRMSD. Therefore, forour SVM-based approach, we used iiRMSD to select similar anddissimilar interfaces for the training sets.The analysis of the terfaces  revealeda to g). The majorityof interactions, however, were mediated by the subunits from four SCOP classes,a, b, c, andd . For each model, we tested bothkernels, polynomial and RBF  is significantly better for both kernels than that one ofNDNNModel. A more detailed analysis revealedthat the difference was mainly due to a higher rate of the true positives(93.7% for ND vs.Model 64.2% forNDNNModel); the rate of the true negativeswas also higher for NDModel (91.0% forND vs.Model 85.9% forNDNNModel).NDModel was also evaluated on a negative set ofnative-native dissimilar interfaces (NNNegative) andcompared with the leave-one-out evaluation ofNDNNModel on the same set. We found that beingtrained on the negative set of native-decoy interface pairs(NDNegative),NDModel cannot generalize well to classifydissimilar native-native interface pairs. It was able to correctly classify only18.5% of the native-native interface pairs;NDNNModel identified 76.6%, which wassimilar to its performance on the native-decoy set. Comparing polynomial and RBFkernels revealed similar performances, although the overall performance of theRBF kernel was slightly better for both SVM models. Finally, we found that theperformance of both similarity measures was several percent better whenconsidering a positive set consisting exclusively of the interfaces at H-level,compared with the positive set consisting of the interfaces at C-level. Forinstance, the cross-validation accuracy when using RBF kernel and testing bothmodels on similar interfaces at H-level was 92.8% forND andModel 84.5% forNDNNModel. Similarly, the cross-validationaccuracy, using the same kernel, while testing both models using similarinterfaces at C-level was 90.5% for NDModeland 77.0% for NDNNModel.The leave-one-out cross-validation was done for each SVM model using the samepositive set and two different negative sets.To further evaluate the obtained SVM interface similarity classifiers, eachclassifier was compared to the state-of-art methods to classify protein-proteininterfaces, SCOPPI NDNNModel, which correctly classified66.4% of dissimilar interfaces. NDModel wastrained to distinguish only between the decoy and native interfaces, and thusperformed poorly on the dissimilar native-native interface pairs.We next tested the two methods on a negative subset of the training set . As bothK = 140, which were the clusters atA-level (A machine learning approach in N), O(N2),and O(N2), where N is the number ofinterfaces.Our next goal was to construct a proof-of-concept of a biologically meaningfulclassification of the interaction interfaces, using the feature-based similaritymeasure. For this purpose, we used the second SVM model, due to its consistencyon both positive and negative datasets of the native-native interfaces. Thesimilarity measure was used to obtain the all-against-all SVM distance matrixfor the set of 2,806×2,806 interfaces. The cluster analysis usingSilhouette method resulted in the number of clustersA-level . FollowiSimilarity-based retrieval of interactioninterfaces under RAVEE and results showed that for20% of queries, RAVEE<0.28Å, for 50% of queriesRAVEE<1.25 Å, and for 80%of queries RAVEE<3.8 Å. Theaverage retrieval time was 0.8 s. The experiments were conducted on a Linuxserver with AMD Opteron dual-core 1000 series processors and 2GB RAM.We next assess the performance of the feature-based similarity measure in thesearch and retrieval of an interface from a large interface dataset. We firstrandomly selected 100 interfaces from the whole dataset and used each interfaceas a query. The remaining 2,706 protein interfaces were used to build an M-Tree homologous interface pairs that are obtainedexclusively from structurally similar binary complexes extracted from 3D Complexdatabase, and (ii) dissimilar interface pairs obtained from the same database. Thepurpose of this dataset is determining which of the structure-based similaritymeasures is more accurate: the one that relies on superposition of the entiresubunits, or the one that relies on the interaction interfaces only. In the seconddataset, we collected as diverse datasets of similar and dissimilar interfaces as wecould reliably get using a structure-based similarity measure. Our protocol removespotential bias in the interaction data, by ensuring that each family of structurallysimilar subunits contributes equally to the dataset. While this is an important stepfor an accurate SVM training, the protocol would not reflect the actual distributionof the interactions across the pairs of homologous families. To account for that, webuilt the third dataset, which not only serves as a test bed for constructing aclassification system of the entire structural interactome, but also allows us tostudy biological phenomena occurring in similar interfaces. All datasets can bedownloaded at: NDModel can be efficientlyused when modeling protein-protein interactions by a comparative approach,e.g., comparative docking, where the modeled interfaces arematched against a database of biological interfaces. Second, we conclude that themain advantages of our approach, compared to the current methods, include bettercoverage and higher accuracy on detecting similar interfaces. On the other hand, ourapproach could further benefit from improving the detection of dissimilarinterfaces.Based on the assessment results of the two SVM classifiers and their comparison withthe state-of-art interface classification systems, we have made several conclusions.First, we suggest that the Hierarchical classification of the interaction interfaces resulted in a significantnumber of 1-member clusters at C- and H-levels. This is not surprising, as theinterfaces clustered into the same C- or H-level cluster have an additionalconstraint: one or both interacting subunits must belong to the same SCOPsuperfamily. The probability of two interactions to have one of the two subunits inthe same SCOP superfamily is small, since the average number of members per eachSCOP superfamily in the considered set of non-redundant interactions (∼2.3) issignificantly smaller than the total number of SCOP Superfamilies for the same set. As a result, the total number of expected clusters with multipleinteractions is expected to be low at C- and H-levels.Hierarchicalclassification of similar interactions under The performance analysis of the hierarchical classification protocol suggests thatexpanding the hierarchical classification to the entire set of protein-proteininteractions is feasible. Indeed, the feature calculation, while taking the mosttime per each interface among the three steps (see section e.g. for comparative docking, where thecandidate interface models are searched against the database of native interfaces orfor functional annotation of novel protein interactions.We have also demonstrated the applicability of the feature-based similarity to theproblem of interface search and retrieval. Specifically, for a query interface onecan accurately and efficiently find a similar interface from a large interfacedataset. This proof-of-concept may have important implications for otherbioinformatics approaches, Finally, for each case study, we have detected and analyzed the charged residueslocated at the interfaces. The analysis has revealed an interesting phenomenon,where the relative positions of charged residues in similar interfaces are eitherswapped between the interacting binding sites or appear in different regions of theinterfaces. The principal role of the charged residues in forming interactioninterfaces has been well studied


Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.http://pppid.biositemap.com/).The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at ( Saccharomyces cerevisiae has been used to develop a eukaryotic unicellular interactome map Cc . This method achieved an accuracy rate of about 0.92 at a precision of about 0.93  Table , suggest93 Table , emphasip-value = 0.000)   Table . In GO ap-value = 0.000)   Table . In GO ahttp://pppid.biositemap.com/).We merged the results of the three methods using cytoscape, and a total of 567,441 PPIs were obtained, and the lowest accuracy rate was greater than 50%, and the coverage of the three results are all 100%. In Figure In the current work, we conducted a comprehensive prediction of porcine PPI inferred from three methods. We studied PPI networks, including Interolog, D-MIST, M-MIST, and a combination of the three. All the four networks were significantly more accurate than we expected. However, the results obtained using the three methods did not match well and showed only small overlaps. The production of this result may be due to that the three methods have different emphasis: Interolog is focus on similarity between sequences; D-MIST emphasizes similarity between domains and similarity between motifs; M-MIST underlines similarity between motifs. The number of overlapping PPIs between D-MIST and M-MIST was 1,902, that between D-MIST and Interolog was 359, and that between M-MIST and Interolog was 51. After verification, each method has certain accuracy. Therefore, the three methods complement each other, and thus provide preliminary reference for related analysis. This finding showed the complexity and diversity of the PPIs, and that the methods have inherently low reproducibility and may not affect some of the interactions. Therefore, for large-scale PPIs studies, combination of these different methods could yield more abundant and accurate results.Comprehensive analysis of the porcine proteome presents an extraordinary challenge. A powerful first step towards addressing this challenge is to develop proteome-scale interaction maps and a framework upon which a complete understanding of biological processes can be obtained. The three methods achieved accuracies of about 0.92, 0.53, and 0.50 and precisions of about 0.93, 0.74, and 0.75, respectively. The Interolog method had the highest accuracy, whereas the two other methods had similar accuracies, exhibiting the reliability of the M-MIST method.Even the most reliable techniques could produce a large number of false-positives, so the three approaches we used would inevitably produce a considerable number of false-positives. These methods suffer from information shortage on time and space. Each of the three methods for identifying porcine PPIs has its own weak points. The Interolog method has high accuracy, but it is only applicable to human, mouse and rat. Higher accuracy rates may be achieved by increasing the number of species used in the method. In D-MIST method, the number of species (204) is sufficient. However, in spite of the presence of PSSM, the analysis only relates to domains with five or more putative interactors. Therefore, domains not frequently found in the set of protein interactions are excluded . In M-MIWe used the iPfam database and GO annotations to assess the reliability of the predicted PPIs. The results showed that 70, 10,495, and 863 interactions were related to Pfam domain-interacting pairs in the iPfam database, whereas an average of only 4.24, 66.79, and 44.26 randomized interactions were related to Pfam domain-interacting pairs in the iPfam database. And on GO annotations, it showed that 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms in the three methods respectively, and that the percentage of PPI pairs sharing GO terms in the predicted PPI network was far higher than the percentage in the 10,000 randomized networks, suggesting that the predicted PPI networks have high statistical significance  [One of the main applications of the PPI network is the prediction of protein functions. In the current research, protein functions were inferred based on their connections in the network . The fun/A1Y2K1) . In ratsSome protein interactions in the cell are transient, and unstable; thus, experiment-based research can hardly capture these interactions. Bioinformatics-based analysis compensates for this shortcoming, with results revealing that the predicted PPI networks are considerably reliable. The current research is an important pioneering work on protein function research.http://www.uniprot.org/) [http://www.hprd.org/download) [http://thebiogrid.org/download.php) [ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam22.0/) database. Through this file and Hmmer-2.3.2, we generated Pfam domain annotations for proteins in our predicted PPIs. Finally, Pfam domain-interacting pairs [ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam21.0/database_files/.About 25,767 pig and 105,828 human protein sequences were obtained from the Uniprot  database (ot.org/)  and saveownload) ; these woad.php)  . BLAST was used for the Interologs, the visualization tool for the biological networks was Cytoscape . Hmmer-2We ran local BLAST using protein sequences of human, mouse and rat with those of pig to obtain the orthologs. The Poisson distribution of Interolog showed that an identity equal to 36% was the cutoff point. To further determine the best cut-off point, we also analyzed other cut-off points Table . Table 1In D-MIST, a PSSM is necessary to predict porcine PPIs. PSSM is a motif descriptor that attempts to capture the intrinsic variability characteristic of sequence patterns. The PSSM principle ascertains the extent of similarity between some sequences and collected sequences, to construct a scoring matrix.q is the observed count for the amino acids at a given location, p is the pseudocount, N is the total number of sequences , and B is the total number of allocated pseudocounts (http://www.people.vcu.edu/~elhaij/IntroBioinf/Scenarios/Scenario5-PSSM.html).Where Doron Betel et al [We downloaded PSSMs from el et al , which cReasons to use M-MIST method are that the result of M-MIST prediction shows a similar accuracy with that of D-MIST prediction, and more importantly, there only exists a small overlap between results of M-MIST and D-MIST prediction. So we used M-MIST to supplement the results of D-MIST to make them more comprehensive. We defined binding motifs as two or more motifs existed in a protein binding a protein at the same time. Each motif was a polypeptide segments of five residues or longer, in which the amino acids side chains were < 4 Å away from the interacting proteins . Two motValidating the porcine PPI network is difficult, because there exists rarely any swine PPIs at present. Several methods have been proposed for the verification of PPI data ,44-47. IThrough Hmmer-2.3.2 and Pfam database , we constructed Pfam domain annotations for proteins in the predicted PPI networks. The default settings were used to conduct Pfam searching. We retained proteins with e-values less than or equal to 0.01. As a result, many proteins were annotated by the Pfam domain in our predicted PPI network. The number of Pfam domain-annotated protein interactions, as well as PPIs related to the Pfam domain-interacting pairs in the iPfam database (release 21.0) was counted  . A goldePPI: protein-protein interaction; D-MIST: domain-motif interactions from structural topology; M-MIST: motif-motif interactions from structural topology; PSSMs: position-specific scoring matrices; DIP: Database of Interacting Proteins; HPRD: Human Protein Reference Database; MINT: the Molecular INTeraction database; BIND: Biomolecular Interaction Network Database; Biogrid: Biological General Repository for Interaction Datasets; MIPS: The MIPS Mammalian Protein-Protein Interaction Database; GSP: golden-standard positive; GSN: golden-standard negative; TP: true positives; TN: true negatives; FP: false positives; FN: false negativesThe authors declare that they have no competing interests.FW, ML and BXS significantly contributed to the present research by designing the conducting the experiment, assembling and verifying the datasets for analysis, and writing the initial draft of the manuscript. DYL supervised the statistical procedures and analysis of the results. HMP, YG, JFH, DLZ revised the manuscript. All authors read and approved the final manuscript.Statistical analysis of the degrees of nodes.Click here for fileThe degrees of nodes.Click here for fileThe overlap between the predicted interactions and domain family pairs from iPfam.Click here for fileThe distribution of the 10,000 randomized networks sharing GO terms.Click here for file


Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.http://www.jjwanglab.org/NRProF/.The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at  E. coli and C. elegans, have 51.17% and 87.92% ambiguous annotations  respectively [Recent advances in high-throughput sequencing technologies have enabled the scientific community to sequence a large number of genomes. Currently there are 1,390 complete genomes . Even thThe definition of biological function itself is enigmatic in biology and highly context dependent -6. This Protein function assignment methods can be divided into two main categories - structure-based methods and sequence-based methods. A protein's function is highly related to its structure. Protein structure tends to be more conserved than the amino acid sequence in the course of evolution ,13. ThusE-value of the BLAST search by Ontoblast [E-value. This problem is tackled by the Protein Function Prediction (PFP) server [The main idea behind sequence-based methods is to compare the query protein to the proteins that are well characterized, and the function of the best hit is directly assigned to the query sequence. GO annotations are assigned to the BLAST search results  for the ntoblast . This wantoblast  and GOtcntoblast  by commu) server , which r) server  and thus) server  is the mp, q and r (whole image), with p ⊂ q ⊂ r as shown in Figure Im(p), Im(q) and Im(r) be the function spaces corresponding to the similarity of the templates in the layers p, q and r respectively. Im(x) gives the similarity between any two patches in the layer x and a mapping set m: that maps the templates from the bottom most layer to the templates in the next layer i.e. p: p → qm, and similarly q: q → rm. Having defined the layers  and the initial layers similarity function Im(p), the algorithm builds a derived kernel on the top of layer r in a bottom-up fashion. The process starts with the calculation of initial reproducing kernel p kon the bottom most layer p as the inner product of its functional space Im(p)×Im(p). Based on the this initial kernel pk, intermediate derived kernel q kis computed on top of the layer q and this in turn is used to compute the final derived kernel r kon the top most layer r, which can help us in the classification of the whole images in layer r. Refer to [Here we present a novel automated protein functional assignment method based on the neural response algorithm . The algRefer to , for theRefer to .In the current context of protein functional characterization, the top layer represents the whole protein sequences and the subsequent layers are constituted of sequence motifs. At each layer similarity is computed between the templates of two successive layers, which are referred to as derived kernels by taking the maximum of the previously computed local kernels in a recursive fashion. Finally a mapping engine is built on the kernels derived from the neural response algorithm to map the query protein to its most probable GO term. A detailed description of the whole methodology is given in the Methods section. as 1 and the protein pairs belonging to different GO terms were labeled as negative, Y = 0. Among such labeled pairs, we randomly selected 3000 positive pairs and 3000 negative pairs and used these labeled protein pairs to train and validate our method. After training the final mapping function, ) f to 1 (share a GO term) if ) ≥ 0.5f to 0 (do not share a GO term) if ) < 0.5f(N.We used the GO terms with no further children (leaf nodes of the GO tree) and their corresponding proteins for the assessment of our method. The rationale for using leaf nodes is that these GO terms are functionally more specific than the GO terms at the higher levels, i.e. no two GO terms should share a common protein and thus can demonstrate the specific function prediction strength of our method. This also addresses the issue of redundancy in the training set. To further fortify our argument we had also addressed the redundancy problem at sequence level by eliminating the redundant sequences that are more than 80% similar in the training set. This was done by using CD-HIT , a progrfolds), with each of the five partitions used exactly once as the validation data. The idea was to check whether our method can correctly classify the pairs, which were not used for training. The values of average accuracy, area under the curve (AUC) and training time of the 5-fold cross validation are reported in Table To evaluate our method we performed 5-fold cross validation i.e. we randomly divided the pool of 6000 labeled protein pairs into five partitions with an equal number of positive and negative labeled pairs. Out of the five partitions, four were used to train the neural response algorithm, and the remaining one partition was used to test the algorithm. This process was repeated for five time  to a pair with two known similar proteins. To test the classification specificity of our method, we have selected 800 proteins (400 pairs) with the first 100 pairs sharing an immediate parent GO term (level 1); second 100 pairs sharing a common parent separated by an edge distance of 2 in the GO tree (level 2). Similarly we have level 3 and 4 datasets with an edge distance of 3 and 4 respectively. As the positive pairs in the training set share a common GO term, we expect our method to classify the protein pairs as positive whose GO terms are the same or the next one in the GO hierarchy and as negative if their respective GO terms are far away. The number of positively classified (similar) pairs in respective subsets is given the Figure Having shown the predominant classification specificity and the 5 fold cross validation results, we further compare the classification accuracy of our method with the PFP and FFPred servers, which are the most sensitive protein function prediction server using GO vocabulary  to date.i p(query protein) and j p(protein in the base dataset) as 1 if they are similar and thereby assigns the GO term of the protein j pto the protein i pbased on the threshold applied on the function )f)f(N) to the query protein, and assigned the GO term of the corresponding most similar (rank 1) protein to the query protein. For a better understanding of the methods, we present the stepwise workout of the algorithm for a human protein Chromodomain Helicase DNA binding protein 1  N where p is the query (CHD1) and i qis the pre-computed neural response of the th iprotein sequence from the initial base set. For illustration here we show the calculation of 3 pair wise neural response vectors (CHD1-AAAS), (CHD1-CHD2) and (CHD1-CDV3) in the Figure )f(N) to the query protein. Since the CHD1-CHD2) f to CHD1. However, in addition to the current state of the algorithm if users wish to consider other non-leaf GO terms, we suggest the users to use the sequence diversity (simply the number of representative sequences after the CD-HIT filtration) in the GO terms associated with the most similar protein to the query protein, as a criteria for assigning the GO term i.e., the GO term with the least sequence diversity is assigned to the query protein. This can be advocated by the fact that the sequence diversity is inversely proportional to the specificity of the GO terms. In the current example by either ways our method had assigned the term GO:0005524 to the CHD1.Next we demonstrate the GO term predictability of our method. Our method labels a protein pair 1 Figure . Firstlye value. FFPred could not predict any GO term because it is limited to only 111 Molecular function and 86 Biological Process categories. Whereas NRProF predicted top 3 similar proteins with the same GO term. The Overall accuracy on the set of 300 proteins is reported in the Table We compared the GO term predictions of our method with PFP and FFPred servers, which are the most sensitive function prediction servers to date. PFP and FFPred servers predict the most probable GO terms for a query protein with a confidence score associated with each of the GO terms. A prediction is considered to be accurate if actual (most specific) GO term of the query protein is ranked among the top 5 probable GO terms by the respective methods. Lack of standalone versions of PFP and FFPred is a serious limitation on the dataset used for comparison. We compiled a dataset of 300 proteins each belonging to the leaf nodes of the GO tree. The prediction results from PFP and FFpred were obtained by manual submissions to the respective servers. Table From Table ) f to 1 (share a GO term) if ) ≥ xf if ) < xf thus we need to take care of the redundancy. This was implicitly addressed by using GO nodes with no children; however we even address this issue at the protein sequence level by using CD-HIT with optimal cut-off, to ensure proper training. An optimal threshold should not be too high or too low, if so the predictions will be biased towards highly similar/dissimilar proteins respectively. In order to observe the influence of this cut-off on the accuracy, we compared the accuracy values on a test set of 2000 protein pairs, with 1000 positive and 1000 negative pairs with respect to five different cut-offs and the results are shown in the Table st pair) based on the respective Intra GO term similarity, given by the similarity between the proteins constituting the 2nd pair, i.e. the 1st pair will be labeled as similar if its similarity is equivalent to the similarity of the 2nd pair (labeled as similar) and vice versa. By this we can bypass the cut-off that needs to be enforced on the simple similarity score for assigning GO terms.We can simply calculate the similarity between a query protein and a known one to assign the corresponding GO term. However with this similarity, we can only use some naive algorithms like k-nearest neighborhood, whose accuracy is not quite satisfactory especially for biological data (proteins), which is essentially multi dimensional. In addition to this, we should artificially enforce a similarity cut-off between the query and the known protein to assign the query protein to its associated GO category. Considering the fact that the intra GO term similarity varies from GO term to GO term it is difficult to set such cut-offs. To conquer this, it is necessary to design a machine learning algorithm that can learn and chose the cut-off based on the similarity between the proteins sharing the same GO term i.e. the similarity cut-off should be high if the intra GO term similarity is high and vice versa. Here, our model assigns the query protein to its associated GO category  and the GO terms, without compromising much on the accuracy. The neural response of a protein with respect to all the templates computed according to the equations 4 (Methods) is nothing but the mapping of a protein (GO term) to the respective templates (motifs). GO-Motif association scores for the same is given by:i NMis the number of proteins (after removing the redundancy by CD-HIT) having a specific motif i associated to a GO term (motif frequency) and i ASis the alignment strength of the respective motif's. We use the product of i NMand i ASto achieve a trade-off between the overrepresentation of a motif to its alignment strength. The detailed calculation is shown in Figure where Our test set is compiled of leaf GO terms and their corresponding proteins with no two GO terms sharing a common protein, to demonstrate the specific function prediction strength of our method. However, up on perusal we found that ~25% of the proteins belong to more than one leaf GO term under the category of molecular function. To analyse the effect of "not including such proteins" on the accuracy, we have compiled a new test set of the same size (300 proteins including proteins belonging to more than one leaf GO term). We perceive that considering proteins belonging to more than one leaf GO term has no negative effect on the GO term predictability. In fact the prediction accuracy is slightly better 89.63% when compared to 86.93% on the actual test set.NRProF, based on the neural response algorithm using the Gene Ontology vocabulary. The neural response algorithm simulates the neuronal behavior of the visual cortex in the human brain. It defines a distance metric corresponding to the similarity by reflecting how the human brain can distinguish different sequences. It adopts a multi-layer structure, in which each layer can use one or multiple types of sequence/structure patterns.Here we present a novel protein function prediction method, NRProF is the first instance of neural response being used in the biological domain. It finds the most similar protein to the query protein based on the neural response N between the query and the target sequences; and thereby assigns the GO term(s) of the most similar protein to the query protein. This is a profound and composite method with the essence of sequential, structural and evolutionary based methods for protein function prediction. The templates from the PRINTS and PFAM database contribute to the functional profiles or signatures (sequence). The mismatch and deletion states in the HMM profiles of the PFAM templates account to the degeneracy due to evolution and the secondary structural information of the match states in the HHM profiles contribute to the structural part. The use of HMM profiles along with the secondary structure information of PROSITE and PFAM sequence motifs to define the neural response gives our method an edge over other available methods to identify the remote homologues, as profile-profile alignments are superior to PSI-BLAST based methods in detecting the remote homologues. Thus NRProF can complement most of the existing methods.st Rank) GO term by our method, 82% is quite a good accuracy. The classification accuracy of 83.8% on a test set of 400 proteins suggests that our method is highly specific in classifying the similar proteins with respect to the relative distance between the respective GO terms. Upon further caparison of our method with the PFP and FFPred servers which are the most sensitive function prediction servers to date, the GO term prediction accuracy of 86.93% evince that our method is more accurate in predicting the specific functions. Thus we conclude that our method is computationally simple yet accurate when compared with the other methods. This is achieved by simulating the neuronal behavior of the visual cortex in the human brain in the form of neural response.Our method is computationally less complex compared with the other methods, as the initial neural response of the proteins in the base dataset with respect to the template library are computed only once and from there the neural response between the query and target is computed with the least computational effort unlike other BLAST/PSI-BLAST based methods. The simple derived kernel adds to the computational simplicity of our method. We validated our method in a 5-fold cross validation fashion and obtained an accuracy of 82%. Considering the criterion that a prediction is valid if and only if the actual GO term is top ranked , we used the sequence motifs from PROSITE  version-1...qm qin the bottom layer. We need to define a non-negative similarity measure i, qj) s ≤ s s 6 profile-profile alignments. To align the template HMM profiles we used HHsearch which is the most sensitive profile-profile alignment tool to date [Our template library in bottom layer consists of HMM profiles from the Pfam database, thus we define the similarity between templates as profile-profile alignment scores. We had 10,257 profiles in the template library, making ~10 to date -35. As a to date . We have to date  and remo to date . Thus we to date  profiles to date  to predi to date . Thus oup in top layer with k template hits denoted by p1...qpk qin bottom layer. PrositeScan [p with respect to a motif q is given by:Consider a protein siteScan  and HMMEsiteScan  are usedm motifs in the template layer the information about the protein p given the templates can be represented by an m-dimensional vector:Now by considering all the i pand the proteins in the base dataset such that we can assign the query protein i pto the GO term(s) associated with the most similar protein jp. To quantize the similarity between pairs i pand jp, we encoded the pair i, pj)  on which we can formulate the mapping engine to map the query protein to its most probable GO term. There are two ways to achieve this, by taking the difference between N(ip) and N(jp) or by simply concatenating them together. As we found that the former method always gives better performance in our algorithm, we thus let:Our goal is to learn the similarity between the query protein i, pj)    or Gaussian radial basis functions (Gaussian kernel). First, we computed the neural response of all the proteins in the base dataset with respect to the template library in top layer. Similar neural response was computed for the query protein sequence as well. Next we computed the pair wise neural response N between the query sequence i and the sequence j (1..n) in the base dataset. The mapping function ) f to 1 (similar) if ) ≥ 0.5f to 0 (non-similar) if ) < 0.5 f was set to 1. In this case the sensitivity of GO term assignments varies with the threshold used (0.5). To overcome this dependency on the threshold, we sorted the proteins in the base dataset into descending order based on their similarity )f(N) to the query protein. We finally extracted the top 5 GO terms and assign them to the query protein. By doing so, we are not only overcoming the threshold dependency problem but also using the ranking )f(N) as the confidence scores for multiple GO terms associated with a single protein.Finally, a rt space  associatviz., Support vector Machines (SVM) [loss function used for training. They use hinge loss and leastsquare loss respectively. The performance of two mapping engines is evaluated in the Results section.We used two popular classification engines es (SVM)  and Leases (SVM)  as the mNo relevant disclosures.HKY collected the data, designed the pipeline, performed evaluations, statistical analysis and wrote the paper. QWX designed and tested the Neural Response algorithm. JW conceptualized the idea, designed the study and wrote the paper. All authors have read and approved the final manuscript.


Identifying protein complexes from protein-protein interaction network is fundamental for understanding the mechanism of cellular component and protein function. At present, many methods to identify protein complexes are mainly based on the topological characteristics or the functional similarity features, neglecting the fact that proteins must be in their active forms to interact with others and the formation of protein complex is following a just-in-time mechanism.Sensitivity, Specificity, and f-measure are significantly improved, when compared with those of the original predicted complexes.This paper firstly presents a protein complex formation model based on the just-in-time mechanism. By investigating known protein complexes combined with gene expression data, we find that most protein complexes can be formed in continuous time points, and the average overlapping rate of the known complexes during the formation is large. A method is proposed to refine the protein complexes predicted by clustering algorithms based on the protein complex formation model and the properties of known protein complexes. After refinement, the number of known complexes that are matched by predicted complexes, The refining method can discard the spurious proteins by protein activity and generate new complexes by just-in-time assemble mechanism, which can enhance the ability to predict complex. In a cell, rather than function individually or in isolation, proteins interact physically with each other to form multisubunit protein complexes that act as sophisticated multimolecular machines, such as the anaphase-promoting complexes, RNA splicing and polyadenylation machinery, protein export and transport complexes. The fund) of a subgraph with n vertices and m edges is generally defined as d=2m/(n(n−1)) , if the boolean function FC is true. If the follow conditions are satisfied, FC is true, otherwise it is false. According to the just-in-time mechanism, a complex j=0,…,K−1(1) (2) S,S+K] for complex C that can make FC be true, we say that the complex can be formed in the continuous active time point set. If there exists no interval for complex C that can make FC be true, we say that the complex cannot be formed in a continuous active time point set. Combined with the active time point set of each protein and this model, a statistic is carried on the 408 known protein complexes of Yeast  to the minimum size of two sets, defined as Equation (6). The overlapping of complex C during the formation interval  is the average of the overlapping rates of time points from S+1 to S+K, defined as Equation (7). The average overlapping of the known complexes is calculated, so do the complexes predicted by representative algorithms, as shown in Table For complex s S, S+i− to the mTo improve the accuracy of complex prediction, we propose a method to refine the protein complexes predicted by existing methods based on protein activity and the protein complex formation model with the just-in-time mechanism.OV, is defined as the fraction of the number of common members in the two clusters to the minimum size of two clusters, shown as Equation (8), where Ci,k is a sub-cluster k at time point i, and Cj,l is a sub-cluster l at time point j. Based on the overlapping of sub-clusters at adjacent time points, a protein complex can be formed step by step. Only the complexes, whose size is greater than 2, will be refined, because the complex with size=2 has only two possible forms after refinement, two singletons or the same complex with no change. The refining method has two steps, including splitting and assembling. The overall framework of the refining method is shown in Figure The active proteins of a predicted complex at each time point can form several sub-clusters based on PPIN topology. The overlapping between two clusters, labeled as n+1 time points, where n is the number of time points in a gene expression profile and an additional special time point ‘0’ is used to contain the proteins whose Active (p)={0}. For each active time point, the proteins which are not active at the time point are excluded, thus the complex can be split into several connected components (sub-clusters) at the time point. In each component, all proteins are active at the time point.The active time set of a complex, defined in Equation (9), is constituted by the active time points of each protein in the complex. During the splitting, an original predicted complex can be split according to The detail of the splitting algorithm is shown in Algorithm 1. The inputs of this algorithm are an original complex which contains proteins and interactions derived from PPIN, and the active time point set of the complex. For each active time point, the proteins which are not active at this time point will be removed from the original complex, and several sub-clusters are generated based on PPIN topology.After splitting, a complex will become many sub-clusters on its whole active time point set. Most sub-clusters are smaller than the original complex. Some sub-clusters may be singletons, some sub-clusters may be identical ones, and some ones are high-overlapping with each other.OV≥T, where T is a threshold to discriminate high overlapping from low overlapping. Active time point ‘0’ is used to denote a special active time point for proteins whose active time points cannot be inferred from the current gene expression profile. These proteins have the potential to be active at some time points when special conditions are prepared. Thus, the sub-clusters at ‘0’ time point can combine with sub-clusters at arbitrary time points with overlapping great than T.Based on the just-in-time mechanism, the assembling of a protein complex is to assemble the sub-clusters with certain overlapping at adjacent time points. The core idea of assembling is to combine the sub-clusters from adjacent time points that satisfy Ci,k at time point i and Cl0, at time point ‘0’ satisfying OV≥T, let Ci,k be the union of Cl0, and itself. After all sub-clusters at each time point have been checked, delete the sub-clusters in the set at time point ‘0’ which have been combined. Secondly, the sub-clusters at time point i and the adjacent time point i+1 are checked. For each Ci,k at time point i, if there exists Ci+1,l that satisfies OV≥T, let Ci+1,l be the union of Ci,k and itself. After all the sub-clusters in the set at time point i set have been checked, the sub-clusters at time point i are deleted if they have been combined. Finally, the left sub-clusters at each time point are considered as new protein complexes. These new protein complexes are assembled according to the time order, connectivity and the overlapping of active proteins. After the assembling process, an original protein complex might become several new protein complexes, or be discarded. New protein complexes tend to be smaller than the original ones. After all original predicted protein complexes are undertaken the splitting and assembling steps, the identical ones in the final results are deleted.The description of assembling algorithm is shown in Algorithm 2. Firstly, let the sub-clusters at time point ‘0’ combine with sub-clusters at other time points. If there exists t1”, protein A, C and E are active and connective with each other, thus they can form a sub-cluster. Protein A, B and E are active at time point “ t2” and form a sub-cluster. At time point “ t3”, only protein B is active. At time point “ t4”, protein B and D are active, but not connective with each other, thus each of them becomes singletons. By combining the high-overlapping sub-clusters in adjacent time points, a new protein complex is generated, which contains A, B, C, and E, as shown in Figure The refinement process of an original complex is illustrated in Figure OS, shown in Equation (10) is used to assess how effectively a predicted complex Pc matches a known complex Kc.In earlier studies ,26,30, tVpc| is the number of proteins in the predicted complex and |VKc| is the number of proteins in the known complex. If a predicted complex Pc that has no common proteins with a known complex Kc, then OS=0. Usually, a predicted complex and a known complex are considered as a match if their overlapping OS is no less than 0.2 , in which MKC will increase after refinement. Therefore, for the algorithms that predict relatively small complexes, the refining method can make the prediction more precise.In Figure Since the spurious proteins of original complexes at each time point are discarded by the refining method, a more precise prediction is supposed to be available for each algorithm. In Figure Sn of refined complexes of each algorithm is higher than that of the original ones. The improvements of Sn of most algorithms are above 15%, except for IPCA, which is about 9%. The potential ability to identify more known complexes is available in each algorithm. The room for the improvement of Sn is determined by the number and the average size of the original complexes predicted by an algorithm. If an algorithm prefers to predict a large number of complexes with small average size, the room for the improvement of Sn is very limited, since a very small number of new different complexes will be generated from the small original complexes. The number of complexes predicted by IPCA is the largest, and the average size is relatively small, thus the space to discard spurious proteins and identify more known complexes by the refining method is limited. If an algorithm tends to identify complexes with relative larger average size, there is great room for improvement, because more new different complexes will be reassembled by the refinement, such as Core-Attachment. As shown in Figure Sp of each algorithm after refinement is also boosted, and they are about 12% in CMC, 8% in CPM, 5% in DPClus, 6% in IPCA, 6% MCL, and 10% in Core-Attachment, respectively. It means although the number of refined complexes of most algorithms are increased, the percentages of MPC are also increased. The f-measure which is based on the increased Sn and Sp is also improved largely after refinement, and the average improvement is about 12%. The f-measure of each algorithms after refinement is enhanced by about 15% in CMC, 16% in CPM, 10% in DPClus, 8% in IPCA, 10% MCL, and 14% in Core-Attachment, respectively, as shown in Figure MKC and MPC.In Figure OV’s threshold T is set as 0.6, while the average co-active rate in the known protein complexes is larger than 0.5. In this subsection, we will discuss the impact of different values of T on the prediction results. Actually, T can vary in the range of . In Figure f-measure of each algorithms varies with different values of T. When T=0, the refined prediction results are the original ones. It is very easy to find out that, the f-measure of six algorithms are enhanced when T>0. We can observe that for all the six algorithms, when T is in the range of , the changes of f-measure is insignificant. Because certain overlapping exists in the sub-clusters and is relatively easy to satisfy the combination condition when T is relatively small, the difference between the original ones and the refined ones is mainly the deletion of some proteins. The PC are not significantly increased, so are the MPC and MKC, which in turns have an influence on the Sn, Sp and f-measure. When T is in the range of (0.5, 1.0], the combination condition is more critical. It makes hard to combine sub-clusters, which makes the prediction more precisely. Thus PC is significantly increased, so are the MPC and MKC. For most algorithms, the f-measures are also significantly increased in this region, except for IPCA. As shown in Figure f-measure of IPCA after refinement is very flat in the range of . This is because the average size of the original complexes predicted by IPCA is relatively small, and the number of the original complexes is very large. On one had, it has tried its best to identify known complexes; On the other hand, the large number of original complexes with small average size limits the spaces to increase MPC, MKC, Sn, Sp and f-measure. Thus, for most algorithms, the value of OV’s threshold T is recommended in the range of .In the refining method, the value of MKC, f-measure, Sn, and Sp of original complexes and the refined ones, the results show that the MKC, f-measure, Sn, and Sp of each algorithm are significantly improved after refinement. Furthermore, it is easy to find out that the performance of algorithms which predict complexes with relative large average size has been greatly improved by our refinement method.Based on the just-in-time mechanism, the protein complex formation model is presented. Furthermore, we analyze the known protein complexes based on the complex formation model and combined with gene expression data, and find out that most complexes can be formed in a continuous time point set and the average overlapping rate of the known complexes during the formation is larger than 0.5. For the complexes which are predicted by clustering methods, only a small portion of them can be formed in a continuous time point set, and the average overlapping rates during the formation are significant lower than that of the known complexes. This paper proposes a method to refine the predicted complexes based on the protein activity and the complex formation model. The refining method contains two steps, splitting and assembling. To evaluate the refining method, we apply it to six algorithms which prefer to predict complexes with different sizes. Through the comparison of the The authors declare that they have no competing interests.JXW and XQP obtained the protein-protein interaction data and gene expression data and designed the refinement method. XQP and QHX evaluated the results. JXW and XQP drafted the manuscript together. QHX, ML and YP participated in revising the draft. All authors have read and approved the manuscript.


For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins.We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions.We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. S. cerevisiae and C. elegans[Understanding of protein functions and protein-protein interactions is one of important topics in the field of molecular biology and bioinformatics. Recently, many researchers have focused on the investigation of amino acid residues of proteins to reveal interactions and contacts between residues -4. If re. elegans. It mean. elegans. On the . elegans,7. Also . elegans,9. On th. elegans. Based o. elegans, and Che. elegans. In this. elegans, which i. elegans  be the appearance frequency of amino acid A at position i in domains mD and nD, and ijf be the joint appearance frequency of a pair of amino acids A at position i in mD and B at position j in nD, where each frequency is divided by the number of paired sequences M in the multiple alignments such that ∑A∈Aif(A) = ∑A,B∈Aijf = 1.Let A| = 21. Then, mutual information for positions i in mD and j in nD is defined as the Kullback-Leibler divergence between the multiplication of appearance frequencies, if(A)jf(B), and the joint appearance frequencies, ijf, as follows.Multiple alignments often include some gaps. Weigt et al. counted the frequencies of gaps as well as amino acids . Therefoi and j are independent from each other, ijf ≈ if(A)jf(B), and ijMI approaches to zero. This means that the two positions are not related with each other in the evolutionary process. If domains mD and nD interact at the positions, it is considered that ijMI becomes high because the positions have coevolved through the evolutionary process in order to keep the interaction. It should be noted that two positions i and j do not always directly interact even if ijMI is high [If frequency distributions of amino acids at positions  is high . HoweverijMI because it can be unnecessarily high depending on distributions of if(A) and jf(B). For that purpose, we make use of ijMI from the joint frequency, ijf, obtained by shuffling at random the combinations of sequences in multiple alignments. In this paper, we repeat the procedure 400 times according to [if(A), jf(B) and ijf should be positive values. Otherwise, we cannot calculate ijMI by using computers. Therefore, we use the following pseudocount as in [However, we need to reduce η is a constant value, in this paper we use η = 1. It should be noted that the sum over all amino acids A, A∈Aif(A) = ∑A,B∈Aijf = 1.where mD and nD, Mmn, to be the maximum of MI over all positions i and j as follows.In order to investigate interactions between proteins, we need MI between domains included in the proteins. Thus, we define MI between domains v〉 means the average of v, i and j are positions of mD and nD, respectively. Since ijMI is calculated to be high for the positions i and j that include many gaps, we exclude positions that include more than 20% gaps as in [where 〈ps as in .mD and nD do not always interact even if the mutual information Mmn is large. For example, Weigt et al. improved MI and proposed direct information (DI) because residues do not always contact with each other even if the MI is large [iP consists of domains D1 and D2 and protein jP consists of domain D3 respectively. If iP and jP do not interact, any pair of  and  does not interact. Deng et al. proposed a probabilistic model for a pair of proteins as follows [iP and jP interact if and only if at least a pair of domains included in the proteins interacts, and events that domains interact are independent from each other, they definedIn this section, we propose a probabilistic model for protein-protein and domain-domain interactions using conditional random fields ,7 becausis large . Most pr follows . By assuijP = 1 means that proteins iP and jP interact, mnD = 1 means that domains mD and nD interact, mnD ∈ ijP means that domain mD is included in protein iP and nD is included in jP and the product in the right hand side is calculated for all domain pairs  included in the protein pair . By transforming equation (5), we havewhere λmn)( = log(1 – Pr(mnD = 1)).where iP, jP) , Pr, and Zij denotes the normalization constant. For instance, equation (8) for ijp = 0 is equivalent to equation (7) in the case that iP, jP) and s = t = 0, otherwise 0.where G with a set of vertices U corresponding to protein-protein interactions ijP, a set of vertices V corresponding to domain-domain interactions mnD, and a set of edges E between U and V as the right figure of Figure ijP ∈ U and mnD ∈ V if and only if mnD ∈ ijP. For the left example of Figure iP, jP) includes domain pairs  and . Then, in the factor graph, the vertex of ijP is connected with vertices of D13 and D23, respectively. Although the vertex of ijP does not have other adjacent vertices than the vertices of D13 and D23, those of D13 and D23 can be connected with other vertices than that of ijPIn Markov random fields, random variables have Markov properties represented as an undirected graph . The facPr(ijP = 0|mnD =t) = 1 – Pr(ijP = 1|mnD = t), it is redundant to consider both s = 0, 1, and it is sufficient to consider only s = 1. Therefore, in order to simplify the model, we substitute iP, jP). Then, we have the following joint probability,Since p means a set of events on protein-protein interactions, ijP = ijp.where M = {Mmn} as given conditional data in order to combine it with the probabilistic model. Then, equation (9) can be written asWe here introduce mutual information between domains whereσ(x) = 1/(1 + ex–) is an increasing function, and c is a positive constant. It should be noted that a negative value, –1, is given to ijP with domain-domain interactions mnD  = ∏ijp∈pijZ(M). By taking the logarithm, we havewhere l(λ). Since log(xe + ye) is a convex function for variables x and y, that is, l(λ) is a concave function, we are able to obtain a global maximum. For maximizing such functions, various methods such as the steepest descent method, Newton’s method, and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) [We estimate the parameters by maximizing the log-likelihood function, o (BFGS)  method hIn the BFGS method, this equation is repeatedly applied for updating a solution.H. sapiens, D. melanogaster, and C. elegans from the DIP database [H. sapiens, 449 interacting pairs that included 562 proteins and 449 domains for D. melanogaster, and 250 interacting pairs that included 602 proteins and 476 domains for C. elegans.We used protein-protein interaction data of database , the fildatabase  as proteMmn, for each pair of domains. Figure Mmn for H. sapiens, D. melanogaster, and C. elegans. We can see from the figure that most domain MIs are distributed in the part of less than about 0.8 for all organisms. It is considered that domains mD and nD with Mmn less than 0.8 may not interact, and domains with Mmn more than 0.8 have more possibilities to interact with each other. Therefore, we set the constant c in equation (12) to be 0.8. Although we tried several values from 0.6 to 1.0 for c, the results were similar to the case of c = 0.8.We used the Pfam database (version 24.0)  to obtaiWe selected non-interacting protein pairs as negative data uniformly at random such that negative data did not overlap with the positive data. The number of negative data was the same as that of positive data for each organism.http://www.chokkan.org/software/liblbfgs/.We used libLBFGS (version 1.9) with default parameters to estimate the parameters S method , and is mnλ that domains mD and nD interact as mnN (mnI) denotes the number of (interacting) protein pairs that include domain pair , and ijρ denotes the interaction strength of protein pair , 0 ≤ ijρ ≤ 1. However, our input interaction data are binary, that is, ijρ takes only 0 or 1. Then, the numerator of the APM method becomes mnI. It means that the APM method for binary interaction data is equivalent to the association method. In the EM method, probabilities mnλ that domains mD and nD interact are estimated by the recursive formula, ijo = 1 denotes that it was observed that proteins iP and jP interact with each other, and fn = 0.8. In this paper, the solution of the association method was given as the initial value In order to evaluate our method, we compared the proposed CRF method with MI and that without MI with the EM method by Deng et al.  and the Pr(ijP = 1|M) of equation (10) for each protein pair in the test dataset and AUC (Area Under ROC Curve) score, where among the test dataset only protein pairs that included at least a parameter estimated from the corresponding training dataset were always used. We repeated 5 times, and took the average. Tables H. sapiens, D. melanogaster, and C. elegans, respectively. An AUC score is the area under an ROC (Receiver Operating Characteristic) curve, and takes a value between 0 and 1. The ROC curve of a random classifier lies on the diagonal line, and the AUC score is 0.5. The ROC curve of a perfect classifier goes through the point , 1 (true positive rate)), and the AUC score is 1. A classifier with the AUC score closer to 1 has better performance. We can see from these tables that the results by the CRF method with MI are better than those by the CRF method without MI, and that the results by the CRF method without MI are better than those by the EM method and the association method. It is also seen that the results by the EM method are almost the same as those by the association method. It might be because the parameters of the EM method were estimated from the solution of the association method and the solution of the EM method already reached a local optimum. Figures We performed five-fold cross-validation, that is, split the data into 5 datasets (4 for training and 1 for test), estimated l1-norm of parameters to the log-likelihood function. Since CRF has an advantage to be able to incorporate large number of features, it remains as a future work to improve the model itself to obtain better accuracy by, for instance, modifying the local feature and adding new features.We proposed novel methods which combine conditional random fields with the domain-based model of protein-protein interactions. In order to give better performance, we introduced mutual information to the probabilistic model. In the improved model, mutual information between domains is given as conditions, where MI between domains is defined as the maximum of MIs between residues in the domains. This method was developed based on the fact that amino acid residues at important sites for interactions have coevolved with each other, and MI has been used for identifying contact residues in interactions. We performed five-fold cross-validation experiments, and calculated AUC for probabilities that two proteins interact. The results suggested that our proposed methods, especially the CRF method with mutual information, are useful. However, the results of AUC for training datasets implied that estimated parameters were overfitting to training datasets. For avoiding that problem, we can improve the methods, for instance, by adding regularization terms, JS proposed the use of mutual information for predicting protein-protein interactions. Methods were developed and implemented by MH. MK and TA participated in the discussion during development of the methods. The manuscript was prepared by MH, JS, and TA.The authors declare that they have no competing interests.


To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term  from long-term  survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM. subnetwork signature that successfully separated GBM patients by their survival times. Our approach to identifying this subnetwork signature also improved our ability to identify its protein products in an independent cohort of patients. In the ongoing search to improve cancer detection and treatment, our work represents a successful strategy for identifying reproducible biomarkers that can more efficiently lead to the discovery of druggable protein targets.Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in adults, and, while the median survival time for treated patients is approximately one year, subgroups of patients respond differently to the same treatments, with some patients showing little improvement and other patients living far longer than expected. These differences in treatment response indicate that the tumors may show molecular differences that we can harness to tailor cancer therapy. To this end, we sought to identify biomarkers of patient survival in GBM. To improve the applicability of our molecular markers to other patient groups, we constrained our markers using maps of protein-protein interactions, and we also employed a unique computational strategy that incorporates patient-to-patient molecular variability into the results. We identified a set of 50 genes comprising a  Glioblastoma multiforme is the most common primary brain tumor in adults and, unfortunately, also the most fatal. While GBMs are categorized histologically, the nature of the disease leads to significant variability in both tumor classification and patient outcome. To more specifically define the disease and simultaneously reveal the etiology, an unbiased search for “molecular signatures” of GBM has been undertaken by several groups Combinatorially dysRegulAted subNEtworks) configuration  distinguishes between the two phenotypes of interest. In this approach, we do not assign a single expression state to a phenotype, but, rather, we search for the set of all states matching a particular phenotype. These expression states are grounded in well-known sets of biological interaction data, as defined by curated protein-protein interaction (PPI) networks.While approaches measuring gene expression across a group can capture gene interaction effects, they often employ summary measures, e.g. averaging, that omit valuable information regarding inter- and intra- patient differences. In this work, we hypothesize that the considerable patient-to-patient variability of GBM can be simplified into molecular networks by identifying molecular “state functions” using the computational method, CRANE  of short-term survival is slightly better than random chance (57%) while the PPV for long-term survival is 74%. The PPV for short-term survival reaches 90% with 5 subnetworks while the maximum observed PPV for long-term survival was 85% with four subnetworks. The cumulative value of using multiple networks – each with a defined set of states – is illustrated in To investigate the reproducibility of CRANE subnetworks in predicting survival, we tested the TCGA-discovered subnetworks' classification performance on an independent GBM dataset published by Lee et al. p-value>0.05) with membership in our survivor groups in the TCGA data.Known molecular subclasses of GBM exhibit differences in survival p-value<1e-6, logrank test), indicating the expected performance of the CRANE classifiers within the test dataset. We then compared CRANE's performance against the four subtypes proposed by Verhaak et al. Though the Verhaak signatures were not designed to segregate patients by survival, the Proneural subtype has slightly longer survival than the other subtypes . Overall, the above tests show that our CRANE gene expression subtypes are distinct from the Verhaak subtypes and represent novel, age-independent subtype classifications for GBM.Given that younger patients tend to have better prognosis p-values for the differential expression of each protein. Using this model, 338 proteins were significantly up- or down-regulated at a p-value≤0.05 . Of the 17 targets of interest that were identified and measured  to identify patterns of proteins that would robustly classify STS from LTS using the significant proteomic targets; for classification, we used the 7 significantly differentially expressed proteomic targets, as well as YWHAQ, which was of borderline significance. This yielded a simple 2-gene protein-level classifier, illustrated in To explore our hypothesis that the use of network topology improves our ability to detect targets at the protein level, we compared the performance of CRANE-identified targets versus that of individual gene markers in identifying dysregulated proteins. The “individual gene markers” refers to a set of the most differentially expressed genes selected without respect to any underlying interaction structure. Specifically, we identified all genes with a fold-change ≥2 between the 86 LTS and STS survivors in the TCGA data and then ranked these genes according to their absolute t-statistic (i.e. the difference in group means divided by the pooled standard deviation). Of the top 200 individual gene markers, only one – ACTG1 – overlapped with the 50-gene subnetwork signature. Thus, 49/50 genes identified using a network-based classifier could not be discovered based on conventional analysis of individual gene markers.As seen in We also explored the proteomic yield of the four Verhaak et al. subtypes. As shown in In this work, we analyzed the mRNA-level heterogeneity of GBMs using protein interaction networks, arriving at a succinct list of 50 genes that predicts patient survival at 80% accuracy. Not only does the unique subnetwork signature show reproducible prediction of patient survival at the mRNA level, it also exhibits protein-level dysregulation that segregates short-term from long-term survivors of glioblastoma – a valuable characteristic in light of recent evidence suggesting that many mRNA-level signatures have questionable classification power and modest biological significance in vitro evidence suggests that this upregulation contributes to resistance against EGFR inhibitors Building upon the success of gene pair classifiers CTNNB1, EP300, STAT3, and TOP1 also appear in the 50-gene subnetwork signature. These genomic alterations are likely to play causative roles in establishing the global state function of the subnetwork signature. β-catenin (CTNNB1), for instance, complexes with N-cadherin to coordinate tumor invasiveness TOP1 is targeted by topoisomerase inhibitors to treat a wide variety of cancers In contrast to proteomic approaches, several groups have worked on classifying the genomic alterations underlying GBM We hypothesized that the underlying network structure would ultimately lead to differences in protein expression between survival groups. Using a mixed model accounting for inter-peptide dependencies within a protein, we identified 7 dysregulated proteins out of a total of 17 detected in the proteomics experiment from the 50-gene subnetwork signature. Though the stochastic nature of proteomics workflows may have discouraged their use as validation platforms, we demonstrate that ultra-long chromatographic gradients coupled with high-resolution mass spectrometers allow us to probe the signaling networks of interest in a high-throughput fashion, with chromatographic reproducibility  sufficieTo gauge how the interaction network influenced our success in identifying dysregulated protein targets, we compared the proteomic performance of CRANE against that of a signature based on differentially expressed individual genes. We found a marked improvement in our ability to detect protein-level changes in identified markers when a network-guided combinatorial algorithm is used to detect mRNA-level dysregulation signatures see , and thep-value = 4.72e-5) Given that the Verhaak et al. subtypes were constructed through hierarchical clustering of gene expression data, we expected that their yield in a proteomics experiment would largely compare to the performance of individual gene markers . While this was the case for Proneural, Classical, and Mesenchymal subtypes, the Neural subtype performed relatively well in predicting differentially expressed proteins, yielding proteomic targets at a rate comparable to the CRANE signatures. This suggests that the Neural subtype contains hidden network structure that boosts the visibility of the group at the protein level and/or that both the CRANE signature and the Neural subtype contain classes of proteins  that are more amenable to proteomic measurement. In support of the latter hypothesis, the top gene ontology (GO) term in the Neural subtype was nucleotide metabolic process  in the Verhaak et al. dataset, including terms such as “regulation of transcription,” “regulation of cell proliferation,” and “cytoskeletal organization” , “I-kappaB kinase/NF-kappaB cascade” , and “regulation of programmed cell death” , all of which were absent or not significant in the Verhaak et al. subtypes  term enrichment of our CRANE signature using DAVID p-value = 1.34e-5) downregulated in the tumors of long-term survivors, suggesting that, even between tumors of the same grade, HSPA9 biology may differentially affect patient survival. Similarly, we found that calnexin (CANX) has 0.74-fold diminished protein expression in long-term survivors, and this result is in line with the observation that CANX expression is significantly correlated with the transition from angiogenesis-independent to angiogenesis-dependent (i.e. more invasive) tumor growth in xenografts Seven subnetwork targets were validated using proteomics, and these proteins have interesting connections to both glioma and cancer. For example, HSPA9 is not only upregulated in a variety of cancers In assessing patient outcomes of GBM, we argue that the most informative prediction is whether or not a patient has a poor prognosis, i.e. is a “short-term survivor,” as this prognosis identifies patients who are poor candidates for the standard of care and for whom more aggressive therapies may be beneficial. To demonstrate the therapeutic potential of proteomic targets, we used CART to identify a decision tree useful in classifying our proteomic cohort. We found that two proteins could effectively classify our cohort of 16 patients with near perfect sensitivity and specificity, though this result may be due to overfitting in our cohort. Nonetheless, this result illustrates how gene expression targets may be translated into clinical proteomics biomarkers.We note that the many of the GBM patients with a poor prognosis in our proteomic validation cohort did not receive the full standard of care: surgery, radiation, and chemotherapy. Consequently, survival classification in our study is not a proxy for response to the standard of care. In future clinical work, efforts should be directed to identifying cancer survivors matched on treatment protocols to allow for the identification of molecular features that render them susceptible to various therapies. While our 50-gene network signature is currently useful for prognostication, analysis of a treatment-matched cohort would potentially allow for the identification of targets to guide therapeutic decision making.http://cancergenome.nih.gov/. Patient data was obtained from TCGA, where clinical data and corresponding microarray data were available for 200 glioblastoma patients de novo GBM, we removed those patients with a pretreatment history, a histologic classification of “treated primary GBM”, or a prior history of glioma. We also excluded patients whose final vital status (living vs dead) was unknown. The remaining patients were separated into two groups based on survival, taking the top 25%  as long-term survivors and the bottom 25%  as short-term survivors.The results published here are in part based upon data generated by The Cancer Genome Atlas (TCGA) pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at J-value. Mutual information is a measure of the reduction in our uncertainty of a patient's phenotype, given observations of the subnetwork's expression state. More precisely, denoting the phenotype random variable with k-dimensional binary random variable representing the expression state of a subnetwork of size k, the mutual information between the expression state and phenotype is defined as A denotes the set of all possible values of X and xp denotes CRANE J-value is defined as the amount of information provided by that particular state on the phenotype, i.e. its contribution to the mutual information between phenotype and the state of the corresponding subnetwork. Namely, for a given state function f for a subnetwork composed of k proteins .In this analysis, we first identified high-scoring subnetworks according to their To test the network features discovered using TCGA, we explored their prediction accuracy using an independent GBM microarray dataset, GSE13041, available via the Gene Expression Omnibus k subnetworks , and test performance was gauged using classification accuracy, calculated as S is the number of correctly predicted short-term survivors, L is the number of correctly predicted long-term survivors, and T is the total number of test samples in the test dataset. We calculated the cumulative classification accuracy for k ranging from 1 to 10, i.e. examining accuracy of the best performing network alone, and then examining the performance of the best two networks, and then the best three networks, etc. Overall classification accuracy reached a maximum of 80% when using k = 5 subnetworks, each composed of size d = 10 genes  was trained on the TCGA data using the top 10 genes .For comparison, we assessed how the four GBM subtypes proposed by Verhaak et al. stratified patient survival in the testing dataset, GSE13041. We first removed pretreated patients from the testing dataset, and the data was then log transformed, median centered, and normalized by each array's standard deviation; gene expression was inferred by averaging probe-level expression. For the 840 genes in the Verhaak et al. GBM subtype classifier, we calculated the Spearman correlation coefficient between the centroid expression profiles (derived from the TCGA dataset) and each sample in the testing dataset, assigning each sample to the subtype with maximum correlation.The Case Comprehensive Cancer Center Institutional Review Board approved this study at a full board review.For proteomic analysis, tumor samples were obtained retrospectively from 18 patients diagnosed with GBM surgically resected at University Hospitals Case Medical Center, with no prior radiotherapy or chemotherapy. Brain tumor tissue was snap frozen 15–30 minutes post tumor resection. One patient was removed as they had an incorrectly documented length of survival; a second patient was removed as their sample's liquid chromatography-mass spectrometry (LC-MS) profile was classified as an outlier by principle component analysis. Of the remaining 16 patients, ten patients had survival time of 9 months or shorter  and 6 patients had survival time of 18 months or longer . Average survival time in months for STS was 4.8 months and for LTS was 25.5 months. Both survival groups had the same proportion of females (60%) and similar average age at diagnosis . The STS had an average post-operative Karnofsky Performance Score (KPS) of 63.3 while the LTS had an average KPS of 80. All LTS patients received post-operative temozolomide and radiation while only 2 and 7 of the STS received post-operative temozolomide and radiation, respectively.30–50 mg of tumor sample was rinsed twice with ice cold phosphate buffered saline (Pierce), and lysed in 450 µL 4% sodium dodecyl sulfate buffered with 50 mM Tris; protease and phosphatase inhibitors (Pierce) were also added. After probe sonication and centrifugation, 150 µL of the lysate was reduced with dithiothreitol (10 mM in solution) and alkylated with iodoacetamide (550 mM in solution). The protein fraction was collected through crashing three times with ice cold acetone. After drying, the protein pellet was resuspended in 200 µL 70% trifluoroacetic acid, and protein concentration was quantified using a Bradford assay. We raised the volume of 150 µg of protein to 100 µL and digested the protein overnight using cyanogen bromide (300 µg). After drying the sample, the peptides were resolubilized in 100 µL of 50 mM ammonium bicarbonate and 4 M urea. The sample was vortexed and then diluted to attain a final urea concentration of 1 M. The samples were then digested overnight with trypsin at 37°C. The digestion was quenched with trifluoroacetic acid, and salts were removed using a C18 column (Nest Group). After drying the cleaned eluent, the sample was resolubilized in 100 µL of 0.1% formic acid in preparation for LC-MS/MS. As successful application of a label-free LC-MS/MS approach hinges on robust and reproducible retention times and MS1 intensity stability, an external spike-in of trypsin digested yeast enolase  was included in all the GBM samples to monitor instrument performance parameters for each LC-MS run; see After lysing the samples and isolating the protein fraction, the 16 samples were analyzed by LC-MS/MS with 4 hour gradients; raw data processing and normalization was performed in Rosetta Elucidator .To identify statistically significant proteomic changes, missing values were imputed using the median intensity per peptide within each survival group, and the data was standard normalized for each peptide. We used a mixed model to compare the group-wise protein intensity differences of interest, with the survival group set as a fixed effect and the peptide set as a random effect, which allowed us to account for the within-protein correlation of the peptides inherent in mass spectrometry-based proteomic experiments Figure S1Retention time plots for three representative proteomic samples illustrating the quality of chromatographic reproducibility. We have shown the 20–220 minute time period out of the total 4 hour run time.(TIF)Click here for additional data file.Figure S2Heatmap of mRNA expression from the TCGA dataset for the 7 differentially expressed proteomic targets. The TCGA tumor samples are rank-ordered by survival, which is shown along the bottom as the number of days till death.(TIF)Click here for additional data file.Figure S3p-value = 0.14).Distributions of the long-term survivors (LTS) and short-term survivors (STS) as defined by the CRANE subnetwork signature. (Top) Survival curves of LTS vs STS. (Bottom) Age distributions of LTS and STS groups. By the log-rank test, there is insufficient evidence to conclude that the age distributions differ ((TIF)Click here for additional data file.Figure S4Classification and regression tree for the proteomic targets. Using CANX and MAPK1 alone, all 6 of our long-term (LT) survivors and 9 of our short-term (ST) survivors are classified correctly.(TIF)Click here for additional data file.Table S1List of 50 genes in the subnetwork signature of survival for primary GBM.(PDF)Click here for additional data file.Table S2and 3 in classifying the independent patient dataset).Classification results of CRANE subnetworks discovered from the TCGA patient cohort and tested on an independent patient dataset (GSE13041). Results reflect classification accuracy of testing incremental combinations of subnetworks Click here for additional data file.Table S3p-value≤0.05) identified in a cohort of 16 GBM patients.Differential expression of proteomic targets ((XLS)Click here for additional data file.Table S4List of significant GO terms overlapping between the Verhaak subtypes and CRANE 50-gene subnetwork signature.(XLS)Click here for additional data file.Table S5p-value≤0.05) in the CRANE 50-gene subnetwork signature.List of GO terms significant ((XLS)Click here for additional data file.