A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. ::: ::: Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. ::: ::: Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip ::: ::: Contact: yongjin.li@gmail.com ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. <s> BIB001 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> Predicting potential drug–target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug–target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug–target associations. It integrates three different networks (protein–protein similarity network, drug–drug similarity network, and known drug–target interaction networks) into a heterogeneous network by known drug–target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug–target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug–target interaction prediction. Excellent performance enables us to suggest a number of new potential drug–target interactions for drug development. <s> BIB002 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> BackgroundDuring the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.MethodsWe combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.ResultsWe apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.ConclusionsWe clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation. <s> BIB003 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> For most problems in science and engineering we can obtain data sets that describe the observed system from variousperspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system’s constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneouslyfactorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone. <s> BIB004 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the biological activity in a single cell line or a single tissue type. We extend this line of research in two directions: (1) an integrative QSAR approach predicting the responses to new drugs for a panel of multiple known cancer cell lines simultaneously and (2) a personalized QSAR approach predicting the responses to new drugs for new cancer cell lines. To solve the modeling task, we apply a novel kernelized Bayesian matrix factorization method. For maximum applicability and predictive performance, the method optionally utilizes genomic features of cell lines and target information on drugs in addition to chemical drug descriptors. In a case study with 116 anticancer drugs and 650 cell lines, we demonstrate the usefulness of the method in several relevant prediction scenarios, differing in the amount of available information, and analyze the importance of various types of drug features for the response prediction. Furthermore, after predicting the missing values of the data set, a complete global map of drug response is explored to assess treatment potential and treatment range of therapeutically interesting anticancer drugs. <s> BIB005 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability to work with multiple side information sources expressed as different kernels. Kernel functions have been introduced to matrix factorization to integrate side information about the rows and columns (e.g., objects and users in recommender systems), which is necessary for making out-of-matrix (i.e., cold start) predictions. We discuss specifically bipartite graph inference, where the output matrix is binary, but extensions to more general matrices are straightforward. We extend the state of the art in two key aspects: (i) A fully conjugate probabilistic formulation of the kernelized matrix factorization problem enables an efficient variational approximation, whereas fully Bayesian treatments are not computationally feasible in the earlier approaches. (ii) Multiple side information sources are included, treated as different kernels in multiple kernel learning that additionally reveals which side information sources are informative. Our method outperforms alternatives in predicting drug-protein interactions on two data sets. We then show that our framework can also be used for solving multilabel learning problems by considering samples and labels as the two domains where matrix factorization operates on. Our algorithm obtains the lowest Hamming loss values on 10 out of 14 multilabel classification data sets compared to five state-of-the-art multilabel learning algorithms. <s> BIB006 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> Abstract Identifying microRNAs associated with diseases (disease miRNAs) is helpful for exploring the pathogenesis of diseases. Because miRNAs fulfill function via the regulation of their target genes and because the current number of experimentally validated targets is insufficient, some existing methods have inferred potential disease miRNAs based on the predicted targets. It is difficult for these methods to achieve excellent performance due to the high false-positive and false-negative rates for the target prediction results. Alternatively, several methods have constructed a network composed of miRNAs based on their associated diseases and have exploited the information within the network to predict the disease miRNAs. However, these methods have failed to take into account the prior information regarding the network nodes and the respective local topological structures of the different categories of nodes. Therefore, it is essential to develop a method that exploits the more useful information to predict reliable disease miRNA candidates. miRNAs with similar functions are normally associated with similar diseases and vice versa. Therefore, the functional similarity between a pair of miRNAs is calculated based on their associated diseases to construct a miRNA network. We present a new prediction method based on random walk on the network. For the diseases with some known related miRNAs, the network nodes are divided into labeled nodes and unlabeled nodes, and the transition matrices are established for the two categories of nodes. Furthermore, different categories of nodes have different transition weights. In this way, the prior information of nodes can be completely exploited. Simultaneously, the various ranges of topologies around the different categories of nodes are integrated. In addition, how far the walker can go away from the labeled nodes is controlled by restarting the walking. This is helpful for relieving the negative effect of noisy data. For the diseases without any known related miRNAs, we extend the walking on a miRNA-disease bilayer network. During the prediction process, the similarity between diseases, the similarity between miRNAs, the known miRNA-disease associations and the topology information of the bilayer network are exploited. Moreover, the importance of information from different layers of network is considered. Our method achieves superior performance for 18 human diseases with AUC values ranging from 0.786 to 0.945. Moreover, case studies on breast neoplasms, lung neoplasms, prostatic neoplasms and 32 diseases further confirm the ability of our method to discover potential disease miRNAs. A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/. <s> BIB007 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects--such as a gene and a disease--can be related in different ways, for example, directly via gene-disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling. ::: Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous data sets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene-disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics. <s> BIB008 </s> A review on machine learning principles for multi-view biological data integration <s> Network-based methods for fusing multiple relational data <s> Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method is an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of $0.8049$ across $341$ diseases and $476$ miRNAs. For five-fold cross-validation, our method achieved an AUC from $0.7970$ to $0.9249$ for $15$ human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com . <s> BIB009
In association studies such as gene-disease associations and genotype-phenotype associations, the relation between two types of objects can also be represented by a matrix denoted by X where x i;j indicates the strength of relation between objects i and j, or by a network where nodes represent objects and (weighted) edges indicate the presence of associations. Thus, the association problems, can be solved by either kernel (relational) matrix factorization methods or graphical methods, even a mixture of both. Based on the number of relationships, association studies can be categorized to two-relational or multi-relational problems. In two-relational association studies, the challenge is how to integrate multiple relational (or adjacency) matrices of two sets of biological entries. For example in gene-disease studies, the question is how to integrate multiple known gene-disease relational matrices obtained by different measurements for inferring new relevant candidate genes or candidate diseases given a pivot set of genes. The kernelized Bayesian matrix factorization method is an effective method to infer a bipartite graph by multiple data source integration BIB005 BIB006 . Recently, Lan et al.  have used this method to infer potential miRNA-disease associations by integrating sequence and functional information of miRNA with semantic and functional information of diseases. Their experimental results demonstrated that this method could not only effectively predict unknown miRNA-disease associations, but also outperformed its competing methods in terms of auROC. A heterogeneous network can be constructed to represent homogeneous associations (such as gene-gene associations) in homo-networks and heterogeneous associations (such as genedisease associations) in heter-networks. Given a network, a random walk is a path that starts at a prespecified node and randomly moves to its neighbor, then to neighbor's neighbor, and so on. Random walks can explain the observed behaviors of many random processes and thus serve as a fundamental model for the recorded stochastic activities. Random walk methods have been applied on either two-relational heterogeneous networks (such as gene-phenotype associations BIB001 , drug-target interactions BIB002 and miRNA-disease associations BIB007 BIB009 ) or multi-relational heterogeneous networks (for example, drug-disease associations BIB003 ) to infer novel candidate relations. Tri-matrix factorizations, combined with network methods, are found useful in association studies of multiple sets of biological entries, where pair-wise associations are represented in relational matrices. Figure 4 is such an example of multirelational associations, where each node represents a set of homogeneous objects and each edge represents a relational matrix. Data fusion approach with penalized matrix trifactorization (DFMF) BIB004 is a model that can integrate multirelational data. Given multiple sets of distinct biological objects fE 1 ; E 2 ; . . . ; E V g, and their relations represented in matrices X i; , where i; j 2 f1; 2; . . . ; Vg, the basic idea is to decompose each given pair-wise association matrix as X ði;jÞ % W ðiÞ S ði;jÞ ðW ðjÞ Þ T , where rows of W ðiÞ and W ðjÞ are the latent factors for object sets E i and E j , respectively, S ði;jÞ governs the interactions between E i and E j . DFMF can only infer new associated objects in a directly given pair of associations, which is essentially a matrix completion problem. Compared with MKL, random forest and relational learning by matrix factorization, DFMF achieved higher auROC for prediction of gene function and prediction of pharmacologic actions. The methodology used in Meduca BIB008 extends DFMF to a solution for inferring the most significant size-k modules of objects indirectly associated to a given set of pivot objectives in a multiplex of association data. As illustrated in Figure 4 , given RR-gene (RR stands for regulatory region), gene-pathway, gene-drug, disease-drug associations, the Meduca method can addresses two questions, for example: (1) Given a pivot set of RRs associated to a subtype of breast cancer, how to detect other RRs that are also significantly associated to this tumor subtype [the so-called candidate-pivot-equivalence (CPE) regime]; (2) Given a pivot set of diseases, how to find the relevant RRs [the so-called candidate-pivot-inequivalence (CPI) regime]. To realize this, Medusa first uses collective matrix factorization (i.e. the DFMF model BIB004 ) to generate latent data matrices for individual associations, and then produces a connection matrix chaining a chosen set of latent data matrices from the source objects to the target objects. For CPE problems, a significant size-k module can be obtained based on connections. For CPI problems, the evaluation of candidate objects is based on visibility. Because there are multiple paths from the source objects to the target objects, Medusa combines all possible connection matrices to compute the scores of each candidate objects for the evaluation of size-k modules. In the prediction of gene-disease associations, Medusa obtained higher auPRC and auROC than random walk.