A critical challenge in Big Data science is the overall lack of data ahalysis platforms available for transforming Big Data into biological knowledge. To address this challenge, we propose a set of interconnected computational tools capable of organizing and analyzing heterogeneous data to support combined inquiries and to de-convolute complex relationships embedded within large-scale data. We demonstrate its utility with a cardiovascular-centric platform that is easily generalizable to similar efforts in other disciplines. Our Center has designed a federated data architecture of existing resources substantiated by a solid and growing user base, and innovations to elevate functionality. Novel crowdsourcing and text-mining methods will extract the wealth of untapped knowledge embedded in biomedical literature, and novel in-depth proteomics analytical tools will unprecedentedly elucidate dynamic protein features. A key strength of our platform will be the rigorous validation using clinical data from Jackson Heart Study and the Healthy Elderly Active Longevity (HEAL;Wellderly) cohorts. Our proposal includes nine scientific aims that address three main focus areas: (i) we will build a new model platform that amalgamates community-supported Big Data resources, enabling data annotations and collaborative analyses;(ii) we will integrate molecular data with drug and disease information, both structured and unstructured, for knowledge aggregation, and (iii) we will create on-the-cloud analytical and modeling tools to power in-depth protein discoveries. Specifically, we will create a novel distributed query system and cloud-based infrastructure that is capable of providing unified access to multi-omics datasets;we will develop computational and crowdsourcing methods to systematically define relationships between genes, proteins, diseases, and drugs from the literature, emphasizing cardiovascular medicine;we will rally community participation and promote awareness of collaborative research through outreach and educational games;we will create a platform to analyze and visualize multi-scale pathway models of genes, proteins, and metabolites;we will develop tools and algorithms to mechanistically model spatiotemporal protein networks in organelles and to. predict higher physiological phenotypes;and we will correlate individual phenotypes, health histories, and multi-scale molecular profiles to examine cardiovascular disease mechanisms. These tools will be implemented, delivered, and executed on the cloud infrastructure to minimize the computational power required of users.

Public Health Relevance

The challenge of biomedical Big Data are multifaceted. Everyday, biomedical researchers face the daunting task of storing, analyzing, and distributing large-scale genomics and proteomics data, and aggregating all information to discern deeper meanings. Only through a coherent effort can we harness copious amounts of unruly genomics and proteomics data for transformation into testable hypotheses that can dovetail with all of scientific research. This Data Science Research Component is designed to address these challenges.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZRG1-BST-R (52))
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Lyster, Peter
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University of California Los Angeles
Los Angeles
United States
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DeLeon-Pennell, Kristine Y; Mouton, Alan J; Ero, Osasere K et al. (2018) LXR/RXR signaling and neutrophil phenotype following myocardial infarction classify sex differences in remodeling. Basic Res Cardiol 113:40
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Lindsey, Merry L; Mouton, Alan J; Ma, Yonggang (2018) Adding Reg3? to the acute coronary syndrome prognostic marker list. Int J Cardiol 258:24-25

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