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.
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.
| Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S et al. (2018) Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine. Circ Res 122:1290-1301 |
| Lindsey, Merry L; Jung, Mira; Yabluchanskiy, Andriy et al. (2018) Exogenous CXCL4 Infusion Inhibits Macrophage Phagocytosis by Limiting CD36 Signaling to Enhance Post-myocardial Infarction Cardiac Dilation and Mortality. Cardiovasc Res : |
| Goldfarb, Dennis; Lafferty, Michael J; Herring, Laura E et al. (2018) Approximating Isotope Distributions of Biomolecule Fragments. ACS Omega 3:11383-11391 |
| Lindsey, Merry L (2018) Reg-ulating macrophage infiltration to alter wound healing following myocardial infarction. Cardiovasc Res 114:1571-1572 |
| 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 |
| Lindsey, Merry L; Kassiri, Zamaneh; Virag, Jitka A I et al. (2018) Guidelines for measuring cardiac physiology in mice. Am J Physiol Heart Circ Physiol 314:H733-H752 |
| Bruggemann, Jacob; Lander, Gabriel C; Su, Andrew I (2018) Exploring applications of crowdsourcing to cryo-EM. J Struct Biol 203:37-45 |
| Lindsey, Merry L; Jung, Mira; Hall, Michael E et al. (2018) Proteomic analysis of the cardiac extracellular matrix: clinical research applications. Expert Rev Proteomics 15:105-112 |
| Mouton, Alan J; Rivera Gonzalez, Osvaldo J; Kaminski, Amanda R et al. (2018) Matrix metalloproteinase-12 as an endogenous resolution promoting factor following myocardial infarction. Pharmacol Res 137:252-258 |
| DeLeon-Pennell, Kristine Y; Iyer, Rugmani Padmanabhan; Ma, Yonggang et al. (2018) The Mouse Heart Attack Research Tool 1.0 database. Am J Physiol Heart Circ Physiol 315:H522-H530 |
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