A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> 'Reverse-phase' protein lysate microarray (RPA) assays use micro-scale, cell lysate dot blots that are printed to a substrate, followed by quantitative immunochemical protein detection, known to be particularly effective across many samples. Large-scale sample collection is a labor-intensive and time-consuming process; the information yielded from RPA assays, however, provides unique opportunities to experimentally interpret theoretical protein networks quantitatively. When specific antibodies are used, RPA can generate 1,000 times more data points using 10,000 times less sample volume than an ordinary western blot, enabling researchers to monitor quantitative proteomic responses for various time-scale and input-dose gradients simultaneously. Hence, the RPA system can be an excellent method for experimental validation of theoretical protein network models. Besides the initial screening of primary antibodies, collection of several hundreds of sample lysates from 1- to 8-h periods can be completed in approximately 10 d; subsequent RPA printing and signal detection steps require an additional 2-3 d. <s> BIB001 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Motivation: The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment. ::: ::: Methods: We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance–covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation–Maximization algorithm. ::: ::: Results: We demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types. ::: ::: Availability: R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm ::: ::: Contact: [email protected] ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. <s> BIB002 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation. <s> BIB003 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO. <s> BIB004 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Similarity network fusion (SNF) is an approach to integrate multiple data types on the basis of similarity between biological samples rather than individual measurements. The authors demonstrate SNF by constructing patient networks to identify disease subtypes with differential survival profiles. <s> BIB005 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes. In this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments. <s> BIB006 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods. <s> BIB007 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based approaches to integrate multiple homogeneous networks <s> We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein-protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service into their own platforms. <s> BIB008
Multi-view data of cohort samples can be integrated in the sample space by network fusion methods. Although these methods are essentially nonlinear MKL models, as they are mainly presented in the context of biological network mining, we discuss them in this separate section. Given multiple networks with identical nodes but different edges, an established idea is to fuse these networks in a final network reflecting common and viewspecific connections. As a typical example of this principle, SNF BIB005 integrates mRNA expression, DNA methylation and miRNA of cohort cancer patients for tumor subtyping and survival prediction. SNF first constructs a sample-similarity network for each view where a node represents a sample and a weighted edge reflects the similarity between two samples. Then, it uses a message-passing-theory-based method to iteratively update each network making it more similar to the other networks. Finally, a patient-similarity network is generated by fusing all individual networks. Essentially, this network-based method falls into the kernel learning principle discussed in the 'Kernel learning and metric learning' section. Kernel clustering methods, for example, spectral clustering, can be applied on the patientsimilarity network to find distinct groups of patients, that is, subtyping. Comparison assessment on multi-omics cancer data showed that SNF outperforms iCluster BIB002 and feature concatenation in terms of Cox log-rank test and a cluster index score. Similarly, using this principle, multi-view data of cohort features can be integrated in the feature space. Here, we take the reconstruction of gene regulatory networks (GRNs) as an example. Given multiple RNA-seq data sets generated under different perturbations of the same cell system, a GRN can be learned for each data set, and then fused by SNF to a final GRN. Likewise, GRNs learned by different algorithms can also be fused by a method like SNF to achieve robust result, which implements the philosophy-'the wisdom of crowds' BIB008 . A similar network fusion method has been proposed in KeyPathwayMinerWeb for pathway enrichment analysis using multi-omics data [79]. Another similar method is called 'FuseNet', which models multiomics data using Markov network allowing non-Gaussian distributions, and represents the model parameters by shared latent factors to collectively infer a holistic gene network  . To learn knowledge from multiple networks with identical nodes but different edges, instead of fusing these networks in a final network, Wu's group recently proposed an energy-based approach to integrate information from multiple networks BIB006 BIB004   BIB007 . Considering a Boolean network with N vertices, there are totally 2 N different configurations. In such a configuration, a node can be labeled as either 1 or 0. If all genes related to a specific disease in a gene network are labeled as 1 and other as 0, then a specific disease corresponds to a specific configuration of the gene network. The probability of the configuration can be calculated by a Boltzmann distribution function pðXÞ with respect to the network configuration as follows: where HðXÞ is the Hamiltonian (energy) of the configuration X, k is a constant parameter and Z is called the partition function and defined as Z ¼ P X2fXg e ÀkHðXÞ , where fXg is a set consisting of all possible configurations of a network. The energy function HðXÞ depends on the network structure and the labeling of the configuration, which can be mathematically expressed as HðXÞ ¼ ÀaN 1 À bN 10 À cN 11 À N 00 ; where N 1 is the number of nodes with label 1, N 00 is the number of edges connecting neighboring nodes with both 0's, N 10 is the number of edges connecting a node with label 0 and another with 1, N 11 is the number of edges bridging two adjacent nodes with label 1 and a, b and c are parameters to be learned. Letting h ¼ fa; b; cg, we can calculate the posterior probability of node n labeled as 1 as follows: where M n0 and M n1 are the numbers of neighbors of node n with label 0 and 1, respectively. For multiple networks, although the labelings of the configuration are the same, their network connections are different. As a result, the energy functions of a specific configuration in multiple networks vary, yet are naturally additive. Suppose that we have V gene networks, the energy of a specific configuration with multiple networks can be calculated as  Accordingly, the posterior probability of node n labeled as 1 can be calculated by  where H ¼ fa; b ðvÞ ; c ðvÞ ; v ¼ 1; . . . ; Vg. Given a set of known genes related to a specific disease, one can learn the parameters in model (12) with a single network or BIB003 with multiple networks by Gibbs sampling algorithms BIB006 BIB004 or maximum likelihood algorithms   . Then the probability that an undetermined gene is related to the specific disease can be calculated by model BIB001 or BIB003 . In both models, only the information of direct neighbors is considered. The results in BIB006  showed that the method with integration of multiple networks outperforms that with single networks in terms of auROC. To include the information of indirect neighbors, a graph-kernel-based method is developed in BIB007 . The comparison in BIB007 reported its superior performance over those methods only using information of direct neighbors.