MOTIVATION: Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS: We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level. Results: We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis, non-small cell lung cancer, prostate cancer and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease. Availability and implementation: The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors. RESULTS: We show the effectiveness of this approach in three different case studies and validate the results in two ways considering the identified genes and interactions between them. We compare our findings with the results of two widely-used methods in the same category as well as the classical approach of selecting differentially expressed (DE) genes in an investigated condition. The results show that 'neighbor-net' analysis is able to report biological mechanisms that are significantly relevant to the given diseases in all the three case studies, and performs better compared to all reference methods using both validation approaches. AVAILABILITY AND IMPLEMENTATION: The proposed method is implemented as in R and will be available an a Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A crucial step in the understanding of any phenotype is the correct identification of the signaling pathways that are significantly impacted in that phenotype. However, most current pathway analysis methods produce both false positives as well as false negatives in certain circumstances. We hypothesized that such incorrect results are due to the fact that the existing methods fail to distinguish between the primary dis-regulation of a given gene itself and the effects of signaling coming from upstream. Furthermore, a modern whole-genome experiment performed with a next-generation technology spends a great deal of effort to measure the entire set of 30,000-100,000 transcripts in the genome. This is followed by the selection of a few hundreds differentially expressed genes, step that literally discards more than 99% of the collected data. We also hypothesized that such a drastic filtering could discard many genes that play crucial roles in the phenotype. We propose a novel topology-based pathway analysis method that identifies significantly impacted pathways using the entire set of measurements, thus allowing the full use of the data provided by NGS techniques. The results obtained on 24 real data sets involving 12 different human diseases, as well as on 8 yeast knock-out data sets show that the proposed method yields significant improvements with respect to the state-of-the-art methods: SPIA, GSEA and GSA. AVAILABILITY: Primary dis-regulation analysis is implemented in R and included in ROntoTools Bioconductor package (versions ≥ 2.0.0). https://www.bioconductor.org/packages/release/bioc/html/ROntoTools.html.

MOTIVATION: The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. RESULTS: We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the rapid advancements in our data acquisition capabilities and the increased availability of gene interaction databases a variety of pathway analysis tools have been proposed. However, all these methods are dependent on the quality of the available pathways. These pathways were designed to describe the general mechanism of a particular disease or biological process. The known pathways encompass the results of many biological experiments and even though they represent our current understanding of those particular biological processes, they are still generally considered sketchy and incomplete. One piece of information that is generally missing regards the role or importance of a gene in a given pathway which we refer to as the gene contribution. We propose here a method, based on genetic algorithms, to objectively quantify the contribution of each gene. Using a pool of 24 data sets from 12 different conditions divided in train and test groups, we show how an impact pathway analysis method achieves significantly better results with the newly estimated gene contributions when compared with both the initial default contributions, as well as randomly selected gene contributions.

Identification of the most impacted signaling pathways in a given condition is a crucial step in understanding the underlying biological mechanism. An impact analysis that is able to take in consideration the structure of a given signaling pathway was proposed to measure the impact on each pathway given a list of differentially expressed (DE) genes and their fold changes. Here, we investigated the utility of incorporating the individual gene significance in the impact analysis of signaling pathways. We propose two alternative models to incorporate the individual gene p-values and compare their performance over a pool of 24 datasets. In addition, the two new models offer the ability to work with the entire set of gene expression measurements, thus eliminating the need to select differentially expressed genes.

Many analysis techniques are currently available to identify the signaling pathways significantly impacted in a given condition. All these approaches calculate a p-value that aims to quantify the significance of the involvement of a given pathway in the condition under study. These p-values were thought to be related to the likelihood of their respective pathways being involved in the given condition, and to be independent. Here we show that this is not true, and that many pathways are not independent and that can considerably affect each other's p-values through a phenomenon we refer to as “cross-talk.” Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. Using real data, we show that in same cases pathways with significant classical p-values are not biologically meaningful, and that some biologically meaningful pathways with insignificant p-values become significant when the cross-talk effects of other pathways are removed. We show that this phenomenon is related to the amount of common genes between different pathways, affecting the most widely used methods for pathway analysis, and we propose an analysis technique that is able to correct the over-enrichment significance of a pathway when the cross-talk effects of other pathways are removed.

MOTIVATION: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. RESULTS: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. AVAILABILITY AND IMPLEMENTATION: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

Two radically different approaches are currently available to identify the signaling pathways that are significantly impacted in a given condition: enrichment analysis and impact analysis. These approaches calculate a p-value that aims to quantify the significance of the involvement of the given pathway in the condition under study. These p-values were thought to be inversely proportional to the likelihood of their respective pathways being involved in the given condition, and hence be independent. Here we show that various pathways can affect each other's p-values in significant ways. Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. In certain circumstances, pathways previously found to be significant with some of the existing methods may not be so. We hypothesize that the phenomenon is related to the amount of common genes between different pathways. Here we present results obtained by analyzing pathways obtained from the KEGG signaling pathways database. However, the same phenomenon is expected to influence the analysis of any pathways from any pathway repository.