The purpose of the proposed project, entitled MIDAs: Multi-modeling and Integrative Data Analytics Training Program, is to extend and enhance a novel Biomedical Informatics training initiative at The Ohio State University that focuses upon the emergent and rapidly growing Biomedical Informatics sub-domains of Translational Bioinformatics (TBI) and Clinical Research Informatics (CRI). This extended program will augment such scholarly focus areas with additional curricula relevant to the application of data science and analytics principles to the two preceding areas, thus accelerating the pace and impact of the research activities conducted by targeted trainees in the era of big data as it applies to biomedicine. MIDAs will leverage the unique scholarly and environmental strengths present at The Ohio State University Wexner Medical Center (OSUWMC), as well as the broader computational and data analytics expertise that spans the campus of The Ohio State University. Trainees will be involved in a combination of didactic and application-oriented instruction modalities, and will pursue independent research projects as a capstone to their curricula. Of note, such research projects will incorporate opportunities for experiential learning and investigation beyond the traditional academic environment through a unique set of public-private partnerships with data analytics focused organizations in the Central Ohio region. The MIDAs training program will house an additional six pre- doctoral trainees, complementing the existing pre- and post-doctoral trainee cohort already engaged in Biomedical Informatics training at The Ohio State University as part of an NLM-funded T15 training award. Our intent with the MIDAs program is to utilize an agile and highly innovative curricula development and evaluation plan, thus allowing for constant program optimization and adaptation to evolving trends and developments in the basic and applied Biomedical Informatics and Data Science knowledge bases.
The purpose of the proposed project, entitled 'MIDAs: Multi-modeling and Integrative Data Analytics Training Program' is to extend and enhance Biomedical Informatics and Data Science training initiatives that focus on the application of 'Big Data' principals to the emergent and rapidly growing sub-domains of Translational Bioinformatics (TBI) and Clinical Research Informatics (CRI). This program will catalyze the formation and engagement of an informatics workforce capable of advancing clinical and translational research through the application of data science theories and methods in order to speed the process by which new basic science discoveries and translated into actionable therapies for human diseases. Of note, this training program will be constructed in a manner that provides students with experience and career paths that span the public, private, and government sectors, thus ensuring the ability of such individuals to make contributions throughout the broad spectrum of organizations engaged in healthcare research and delivery.
|Wells, Rebecca; Kite, Bobbie; Breckenridge, Ellen et al. (2018) Community Mental Health Center Integrated Care Outcomes. Psychiatr Q 89:969-982|
|Nasser, Mohd W; Datta, Jharna; Nuovo, Gerard et al. (2018) Down-regulation of micro-RNA-1 (miR-1) in lung cancer. Suppression of tumorigenic property of lung cancer cells and their sensitization to doxorubicin-induced apoptosis by miR-1. J Biol Chem 293:12945|
|Abrams, Zachary B; Zucker, Mark; Wang, Min et al. (2018) Thirty biologically interpretable clusters of transcription factors distinguish cancer type. BMC Genomics 19:738|
|Sinicropi-Yao, Sara L; Amann, Joseph M; Lopez, David Lopez Y et al. (2018) Co-Expression Analysis Reveals Mechanisms Underlying the Varied Roles of NOTCH1 in NSCLC. J Thorac Oncol :|
|Sharpnack, Michael F; Ranbaduge, Nilini; Srivastava, Arunima et al. (2018) Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma. J Thorac Oncol 13:1519-1529|
|Motiwala, Tasneem; Majumder, Sarmila; Ghoshal, Kalpana et al. (2018) PTPROt inactivates the oncogenic fusion protein BCR/ABL and suppresses transformation of K562 cells. J Biol Chem 293:3589|
|Patterson, Emily S; Dewart, Courtney M; Stevenson, Kurt et al. (2018) A MIXED METHODS APPROACH TO TAILORING EVIDENCE-BASED GUIDANCE FOR ANTIBIOTIC STEWARDSHIP TO ONE MEDICAL SYSTEM. Proc Int Symp Hum Factors Ergon Healthc 7:224-231|
|Ding, Hao; Sharpnack, Michael; Wang, Chao et al. (2018) Integrative cancer patient stratification via subspace merging. Bioinformatics :|
|Badawi, Mohamed; Kim, Jihye; Dauki, Anees et al. (2018) CD44 positive and sorafenib insensitive hepatocellular carcinomas respond to the ATP-competitive mTOR inhibitor INK128. Oncotarget 9:26032-26045|
|Latchana, Nicholas; DiVincenzo, Mallory J; Regan, Kelly et al. (2018) Alterations in patient plasma microRNA expression profiles following resection of metastatic melanoma. J Surg Oncol 118:501-509|
Showing the most recent 10 out of 38 publications