Mounting amounts of diverse biomedical data have been generated. Extracting meaningful information from these datasets has relied on the efforts of informaticians, who are extensively trained in the computer science realm, with little to no training in biology. Similarly, biologists in general are not proficient to analyze, annotate, and translate their large datasets into valuable biomedical insights. In addition, there has been an overall lack of public understanding for the importance of Big Data science, hindering the enthusiasm to advance data science in the biomedical field. To bridge the gaps that exist among data generation, interpretation and awareness, our training program will provide critical data science education to current biomedical researchers, expand the data science workforce in the biomedical field, and elicit a broad public recognition of data science. Accordingly, we have engineered an integrated training program with four specific aims: 1) To empower current biomedical researchers with the ability to manage and interpret Big Data by gaining proficiency in utilizing data science software tools;2) To utilize the training component as an interactive testing field for software packages developed by the Data Science Research (DSR) component. User critiques/feedback will refine and transform software tools to a professional grade, facilitating the community to capture the full value of Big Data;3) To cultivate a new generation of developers with transdisciplinary expertise in both computational biology and biomedical informatics;and 4) To heighten public awareness of and enthusiasm for the substantial opportunities embedded within computational biology, which has the potential to transform biomedical research and medicine. To achieve these aims, we have constructed three trainee-oriented modules: Biomedical Researcher /User-Oriented Module, Big Data Science Researcher-Oriented Module, and General Public-Oriented Module. A trans-institutional collaboration has been organized (i.e., UCLA, TSRI, UMMC, and EMBL-EBI), and all components have demonstrated outstanding track records in education. This collaboration will ensure successful execution of the training component substantiated by distinguished experts and meritorious educators from a wide breadth of disciplines, spanning -omics, bioinformatics, and computational science.

Public Health Relevance

The challenges of biomedical Big Data are multifaceted. Advances in biomedical sciences using Big Data will require an adequate workforce with the appropriate data science expertise and skills, including those in computational biology, biomedical informatics, and related areas. Users of Big Data software tools and resources must be trained to use them well. This Training Component is designed to address these challenges.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54GM114833-01
Application #
8910930
Study Section
Special Emphasis Panel (ZRG1-BST-R (52))
Program Officer
Lyster, Peter
Project Start
Project End
Budget Start
2014-09-29
Budget End
2015-04-30
Support Year
1
Fiscal Year
2014
Total Cost
$348,914
Indirect Cost
$47,069
Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
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
Brooks, Heddwen L; Lindsey, Merry L (2018) Guidelines for authors and reviewers on antibody use in physiology studies. Am J Physiol Heart Circ Physiol 314:H724-H732
Yates 3rd, John R (2018) Content Is King: Databases Preserve the Collective Information of Science. J Biomol Tech 29:1-3
Sallam, Tamer; Sandhu, Jaspreet; Tontonoz, Peter (2018) Long Noncoding RNA Discovery in Cardiovascular Disease: Decoding Form to Function. Circ Res 122:155-166
Lindsey, Merry L; Bolli, Roberto; Canty Jr, John M et al. (2018) Guidelines for experimental models of myocardial ischemia and infarction. Am J Physiol Heart Circ Physiol 314:H812-H838
Ma, Yonggang; Mouton, Alan J; Lindsey, Merry L (2018) Cardiac macrophage biology in the steady-state heart, the aging heart, and following myocardial infarction. Transl Res 191:15-28
Fabregat, Antonio; Sidiropoulos, Konstantinos; Viteri, Guilherme et al. (2018) Reactome diagram viewer: data structures and strategies to boost performance. Bioinformatics 34:1208-1214
Lindsey, Merry L; Gray, Gillian A; Wood, Susan K et al. (2018) Statistical considerations in reporting cardiovascular research. Am J Physiol Heart Circ Physiol 315:H303-H313
Liem, David A; Murali, Sanjana; Sigdel, Dibakar et al. (2018) Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease. Am J Physiol Heart Circ Physiol 315:H910-H924
Mouton, Alan J; DeLeon-Pennell, Kristine Y; Rivera Gonzalez, Osvaldo J et al. (2018) Mapping macrophage polarization over the myocardial infarction time continuum. Basic Res Cardiol 113:26

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