A central challenge for systems biology is to transform the data deluge characteristic of the post- genomic world, into the kind of knowledge that will lead to advances in biomedical science and clinical medicine. The transformation requires computer systems that will enable organizing, mining, analyzing and displaying the data in a manner that both informs and drives new hypotheses. VisANT (a visualization, mining system) and Predictome (an integrated database component) form an online graphical workspace and interface for principled integration, mining, discovery, and analysis of molecular networks. The system currently includes, or seamlessly accesses, data sets based on some 70 laboratory and computational methods, and more than 100 species. It has a distinguished and active advisory Board, more than 1000 registered users, is accessed by more than 1100 independent sites per month and has been cited approximately 300 times in the scientific literature. We propose substantially expanding the range of VisANT by including metabolic, chemical reaction, drug and disease related data. Equally importantly, a central goal of VisANT has been integration, and we will substantially extend its integrative capabilities, enabling the identification of disease related pathways, of similarities in mechanisms between phenotypically distinct diseases, and the identification of potential lead compounds for therapy. At the same time we propose substantial expansion of our outreach activities, and movement toward a true many-to many communication and discovery system.

Public Health Relevance

Project Narrative One of the major challenges facing biomedical science is conversion of massive and rapidly increasing data, into medically useful knowledge. VisANT-Predictome is a computer system designed to facilitate that transformation through the use of advanced information technologies, mathematical statistics, and a platform to stimulate self organization of collaborative research.

National Institute of Health (NIH)
National Center for Research Resources (NCRR)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-H (50))
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Brazhnik, Olga
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Boston University
Schools of Engineering
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Hu, Zhenjun (2014) Using VisANT to Analyze Networks. Curr Protoc Bioinformatics 45:8.8.1-39
Richens, Joanna L; Morgan, Kevin; O'Shea, Paul (2014) Reverse engineering of Alzheimer's disease based on biomarker pathways analysis. Neurobiol Aging 35:2029-38
Shigemizu, Daichi; Hu, Zhenjun; Hung, Jui-Hung et al. (2012) Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer. PLoS Comput Biol 8:e1002347
Hung, Jui-Hung; Yang, Tun-Hsiang; Hu, Zhenjun et al. (2012) Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinform 13:281-91
Huang, Chia-Ling; Lamb, John; Chindelevitch, Leonid et al. (2012) Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge. BMC Bioinformatics 13:46
Hung, Jui-Hung; Whitfield, Troy W; Yang, Tun-Hsiang et al. (2010) Identification of functional modules that correlate with phenotypic difference: the influence of network topology. Genome Biol 11:R23
Demir, Emek; Cary, Michael P; Paley, Suzanne et al. (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28:935-42
Linghu, Bolan; Snitkin, Evan S; Hu, Zhenjun et al. (2009) Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol 10:R91
Hu, Zhenjun; Hung, Jui-Hung; Wang, Yan et al. (2009) VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res 37:W115-21
Hu, Zhenjun; Snitkin, Evan S; DeLisi, Charles (2008) VisANT: an integrative framework for networks in systems biology. Brief Bioinform 9:317-25