A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Protein-protein interactions play a key role in many biological systems. High-through- put methods can directly detect the set of interact- ing proteins in yeast, but the results are often incomplete and exhibit high false-positive and false- negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, ap- proaches, and implementations varied. Further- more, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co-complex relationship, and (3) path- way co-membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity-based k-Nearest-Neigh- bor, Naive Bayes, Decision Tree, Logistic Regres- sion, and Support Vector Machine. For all classifi- ers, the three prediction tasks had different success rates, and co-complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consis- tently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting func- tion of the RF tree structure, the Gini index, to estimate feature importance. Second, we deter- mined classification accuracy when only the top- ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expres- sion is consistently the most important feature for all three prediction tasks, while the protein interac- tions identified using the yeast-2-hybrid system were not among the top-ranking features under any con- <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> A map of protein-protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein-protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein-protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40,753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, approximately 35% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein-protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database’, available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14 312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein–protein interactions documented in the database are integrated into Entrez Gene records and listed in the ‘HIV-1 protein interactions’ section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and subsequently to the design of new therapeutic approaches. This work is the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins. We propose a supervised learning framework, where multiple information data sources are utilized, including cooccurrence of functional motifs and their interaction domains and protein classes, gene ontology annotations, posttranslational modifications, tissue distributions and gene expression profiles, topological properties of the human protein in the interaction network and the similarity of HIV-1 proteins to human proteins’ known binding partners. We trained and tested a Random Forest (RF) classifier with this extensive feature set. The model’s predictions achieved an average Mean Average Precision (MAP) score of 23%. Among the predicted interactions was for example the pair, HIV-1 protein tat and human vitamin D receptor. This interaction had recently been independently validated experimentally. The rank-ordered lists of predicted interacting pairs are a rich source for generating biological hypotheses. Amongst the novel predictions, transcription regulator activity, immune system process and macromolecular complex were the top most significant molecular function, process and cellular compartments, respectively. Supplementary material is available at URL www.cs.cmu.edu/~oznur/hiv/hivPPI.html <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Motivation: Protein–protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled). ::: ::: Results: We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information. ::: ::: Availability: http://www.cs.cmu.edu/~qyj/HIVsemi ::: ::: Contact: qyj@cs.cmu.edu <s> BIB006 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Background ::: Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein–protein interactions (PPIs), they have primarily focused on intra-species protein–protein interactions. <s> BIB007 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other. <s> BIB008 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-1: classification-based approaches <s> Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research. <s> BIB009
In BIB005 , a supervised learning framework is presented for predicting PPIs between HIV-1 and human proteins. It was the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins. In this work, the task of predicting PPIs is formulated as a binary classification problem where each protein pair belongs to either the interaction or non-interaction class. A Random Forest Classifier (RF) is trained with a rich feature set derived from several biological information sources. Total 35 features are recognized based on human intra-PPIs and other information sources. Some features precisely capture the information from HIV-1 human protein pairs, while others are related to only human or HIV-1 proteins, and some are derived from the human interactome. As these extracted features are noisy and redundant, therefore a RF BIB001 has been used for the classification task. RF classifier has already been established to be useful in predicting intra-species PPIs BIB002   . It is also suitable for handling the scenarios where feature set is noisy and redundant. For building the trees in RF in BIB001 , a node is chosen to be split, when the attribute causes highest decrease in the GINI index. All the features are ranked depending on the Gini importance of the RF classifier. The topological properties like degree and betweenness centrality of the graph, and the GO neighbour similarity features got the top rank. Degree is coming out here to be a top-ranked feature. It is quite expected because degree is the property of a single node and provides a good prior interaction probability for the prediction purpose. The other top-ranked features with respect to the Gini importance are pairwise GO similarity, clustering coefficient, GO neighbour function, location features etc. It is pertinent that Gene Ontological features come out to be top-ranked features. It is possibly because of the importance of GO similarity measures or other GO-related features that contribute greater knowledge towards interaction prediction  BIB003 . The authors of BIB005 have extended their work in BIB006 by integrating a semi-supervised multi-tasking approach to improve the accuracy of the previous predictive model. In this work, they reduced their feature set but while improving the accuracy by considering 'weakly labelled' PPIs. The 'weakly labelled PPIs' are not experimentally confirmed PPIs, but are likely to have interaction relationships. Here the motivation of taking these weakly labelled PPIs is the insufficiency of confirmed labelled PPIs. The PPIs in the HHPID BIB004 , which are retrieved from different scientific literature, may not have enough evidence of being true positive labelled interaction. But these interactions may be a possible candidate to contribute greater knowledge towards the PPI prediction. To incorporate the 'weakly labelled PPIs' information with 'true positive labelled PPIs' the authors in BIB006 have used the multi-task learning framework. For selecting the positive labelled PPIs and partially labelled PPIs (or weakly labelled PPIs), they collected experts' opinions for improving the quality of the data set. The recruited HIV experts contributed their knowledge to annotate the interactions of HHPID in truly positive label and partially positive label. The 158 pairs, annotated by HIV-1 experts, are considered here as 'gold standard positive pairs' and the remaining 2119 pairs are labelled as partially positive. For choosing the negative set, approximately 16 000 random protein pairs are collected excluding the pairs existing in the HHPID. To compensate the effect of random sampling of negative training set, 5-fold cross validation (CV) with 20 repeated CV runs are performed to obtain the average performance scores. In another study, Dyer et al. BIB007 proposed a supervised classification technique using Support Vector Machine (SVM) with a linear kernel for predicting PPIs between HIV-1 and human proteins. For choosing negative examples, they also used random pairing of HIV-1 and human proteins. In doing so, they ensured that no randomly generated protein pair was already known to interact. They generated three sets of negative examples containing 25, 50 and 100 times the number of the positive examples and measured the performance of SVM. The three types of features used in this work are domains, protein sequence k-mers and properties in the intra-species human PPI network. In BIB009 , a probability weighted ensemble transfer learning model is proposed to predict PPIs between HIV-1 and human proteins. Here, the homolog GO information is incorporated to cope up with the three major problems of PPI prediction, viz., data scarcity, data unavailability and negative data sampling BIB009 . SVM is adopted here as the individual classifiers of the ensemble model. In this work it is demonstrated that homolog GO information is effective to enrich or substitute the target GO information. Irrespective of all the classification techniques reviewed here, the task of choosing the negative examples is generally observed as a common problem. In most of the cases for choosing non-interacting pairs, a set of protein pairs are extracted at random from the set of all protein pairs. However, there is no evidence that these chosen pairs do not interact at all. As a result, the classification task remains sensitive towards the chosen negative samples. In BIB008 , the negative to positive ratio is kept as 100:1, and this ratio is optimized in each CV step in the training phase. In BIB007 , the negative to positive ratio was varied as 25:1, 50:1 and 100:1, and using each of these ratios the performance of SVM classifier is measured under different feature sets. In BIB006 , a remedy of this problem was observed by performing 5-fold CV with 20 repeated CV runs for compensating the effect of random sampling of negative training set. Here a clear separation is maintained for the negative labelled data and partially positive labelled data set. A classifier is trained to distinguish partial positive examples from negative examples. This refinement of data set leads to a significant improvement in identification of interacting protein pairs.