Ranga Chandra Gudivada

Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships

BackgroundDiscovering that drug entities already approved for one disease are effective treatments for other distinct diseases can be highly beneficial and cost effective. To do this predictively, our conjecture is that a semantic infrastructure linking mechanistic relationships between pharmacologic entities and multidimensional knowledge of biological systems and disease processes will be highly enabling.ResultsTo develop a knowledge framework capable of modeling and interconnecting drug actions and disease mechanisms across diverse biological systems contexts, we designed a Disease-Drug Correlation Ontology (DDCO), formalized in OWL, that integrates multiple ontologies, controlled vocabularies, and data schemas and interlinks these with diverse datasets extracted from pharmacological and biological domains. Using the complex disease Systemic Lupus Erythematosus (SLE) as an example, a high-dimensional pharmacome-diseasome graph network was generated as RDF XML, and subjected to graph-theoretic proximity and connectivity analytic approaches to rank drugs versus the compendium of SLE-associated genes, pathways, and clinical features. Tamoxifen, a current candidate therapeutic for SLE, was the highest ranked drug.ConclusionThis early stage demonstration highlights critical directions to follow that will enable translational pharmacotherapeutic research. The uniform application of Semantic Web methodology to problems in data integration, knowledge representation, and analysis provides an efficient and potentially powerful means to allow mining of drug action and disease mechanism relationships. Further improvements in semantic representation of mechanistic relationships will provide a fertile basis for accelerated drug repositioning, reasoning, and discovery across the spectrum of human disease.

Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge

Most common chronic diseases are caused by the interactions of multiple factors including the influences and responses of susceptibility and modifier genes that are themselves subject to etiologic events, interactions, and environmental factors. These entities, interactions, mechanisms, and phenotypic consequences can be richly represented using graph networks with semantically definable nodes and edges. To use this form of knowledge representation for inferring causal relationships, it is critical to leverage pertinent prior knowledge so as to facilitate ranking and probabilistic treatment of candidate etiologic factors. For example, genomic studies using linkage analyses detect quantitative trait loci that encompass a large number of disease candidate genes. Similarly, transcriptomic studies using differential gene expression profiling generate hundreds of potential disease candidate genes that themselves may not include genetically variant genes that are responsible for the expression pattern signature. Hypothesizing that the majority of disease-causal genes are linked to biochemical properties that are shared by other genes known to play functionally important roles and whose mutations produce clinical features similar to the disease under study, we reasoned that an integrative genomics-phenomics approach could expedite disease candidate gene identification and prioritization. To approach the problem of inferring likely causality roles, we generated Semantic Web methods-based network data structures and performed centrality analyses to rank genes according to model-driven semantic relationships. Our results indicate that Semantic Web approaches enable systematic leveraging of implicit relations hitherto embedded among large knowledge bases and can greatly facilitate identification of centrality elements that can lead to specific hypotheses and new insights.

Computational drug repositioning through heterogeneous network clustering

BackgroundGiven the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.ResultsUsing known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.ConclusionsPrevious computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.

GenomeTrafac: a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs.

Transcriptional cis-regulatory control regions frequently are found within non-coding DNA segments conserved across multi-species gene orthologs. Adopting a systematic gene-centric pipeline approach, we report here the development of a web-accessible database resource—GenomeTraFac ()—that allows genome-wide detection and characterization of compositionally similar cis-clusters that occur in gene orthologs between any two genomes for both microRNA genes as well as conventional RNA-encoding genes. Each ortholog gene pair can be scanned to visualize overall conserved sequence regions, and within these, the relative density of conserved cis-element motif clusters form graph peak structures. The results of these analyses can be mined en masse to identify most frequently represented cis-motifs in a list of genes. The system also provides a method for rapid evaluation and visualization of gene model-consistency between orthologs, and facilitates consideration of the potential impact of sequence variation in conserved non-coding regions to impact complex cis-element structures. Using the mouse and human genomes via the NCBI Reference Sequence database and the Sanger Institute miRBase, the system demonstrated the ability to identify validated transcription factor targets within promoter and distal genomic regulatory regions of both conventional and microRNA genes.

Integrative Systems Biology Approaches to Identify and Prioritize Disease and Drug Candidate Genes

Although a number of computational approaches have been developed to integrate data from multiple sources for the purpose of predicting or prioritizing candidate disease genes, relatively few of them focus on identifying or ranking drug targets. To address this deficit, we have developed an approach to specifically identify and prioritize disease and drug candidate genes. In this chapter, we demonstrate the applicability of integrative systems-biology-based approaches to identify potential drug targets and candidate genes by employing information extracted from public databases. We illustrate the method in detail using examples of two neurodegenerative diseases (Alzheimers and Parkinsons) and one neuropsychiatric disease (Schizophrenia).

PhenoHM: human–mouse comparative phenome–genome server

PhenoHM is a human–mouse comparative phenome–genome server that facilitates cross-species identification of genes associated with orthologous phenotypes (http://phenome.cchmc.org; full open access, login not required). Combining and extrapolating the knowledge about the roles of individual gene functions in the determination of phenotype across multiple organisms improves our understanding of gene function in normal and perturbed states and offers the opportunity to complement biologically the rapidly expanding strategies in comparative genomics. The Mammalian Phenotype Ontology (MPO), a structured vocabulary of phenotype terms that leverages observations encompassing the consequences of mouse gene knockout studies, is a principal component of mouse phenotype knowledge source. On the other hand, the Unified Medical Language System (UMLS) is a composite collection of various human-centered biomedical terminologies. In the present study, we mapped terms reciprocally from the MPO to human disease concepts such as clinical findings from the UMLS and clinical phenotypes from the Online Mendelian Inheritance in Man knowledgebase. By cross-mapping mouse–human phenotype terms, extracting implicated genes and extrapolating phenotype-gene associations between species PhenoHM provides a resource that enables rapid identification of genes that trigger similar outcomes in human and mouse and facilitates identification of potentially novel disease causal genes. The PhenoHM server can be accessed freely at http://phenome.cchmc.org.

A new synonym-substitution method to enrich the human phenotype ontology

BackgroundNamed entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem.ResultsThis paper presents an automated synonym-substitution method to enrich the Human Phenotype Ontology (HPO) with new synonyms. The approach is mainly based on both the lexical properties of the terms and the hierarchical structure of the ontology. By scanning the lexical difference between a term and its descendant terms, the method can learn new names and modifiers in order to generate synonyms for the descendant terms. By searching for the exact phrases in MEDLINE, the method can automatically rule out illogical candidate synonyms. In total, 745 new terms were identified. These terms were indirectly evaluated through the concept annotations on a gold standard corpus and also by document retrieval on a collection of abstracts on hereditary diseases. A moderate improvement in the F-measure performance on the gold standard corpus was observed. Additionally, 6% more abstracts on hereditary diseases were retrieved, and this percentage was 33% higher if only the highly informative concepts were considered.ConclusionsA synonym-substitution procedure that leverages the HPO hierarchical structure works well for a reliable and automatic extension of the terminology. The results show that the generated synonyms have a positive impact on concept recognition, mainly those synonyms corresponding to highly informative HPO terms.

ailSemantic Web-based data representation and reasoning applied to disease mechanism and pharmacology

To pursue a systematic approach to the discovery of novel and inferable relationships between drugs and diseases based on mechanistic knowledge, we have sought to apply semantic Web-based technologies to integrate heterogeneous data from pharmacological and biological domains. We have devised a knowledge framework, Disease-Drug Correlation Ontology (DDCO), constructed for semantic representation of the key entities and relationships. A collection of prior knowledge sets including pharmacological substance, drug target, pathway, disease and clinical features, and all interlinking properties were integrated using an RDF (resource description framework) model derived from the semantic elements defined in the DDCO framework. Using the resulting RDF graph network, ontology-based mining and queries could identify embedded associations in this genome-phenome-pharmacome network. Several use-cases demonstrated that potentially powerful rewards could be obtained through semantic integration based on principles of drug action modeling.