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MIDAs: Multi-Modeling and Integrative Data Analytics Training Program

Synopsis of the Program

The MIDAs Training Program is funded by the NIH Big Data to Knowledge Initiative in conjunction with the National Library of Medicine. Trainees who participate in this program will pursue a doctoral degree in the biomedical sciences graduate program, pursue cross-disciplinary training in data science and data analytics tools, methods, and theories through targeted coursework in the Department of Biomedical Informatics and apply this knowledge within real-world experiences through lab rotations, internships, and collaborative projects done in conjunction with some of the leading analytical groups in the Central Ohio region.

Program Background

The delivery of personalized healthcare is predicated on the application of the best available scientific knowledge to the practice of medicine in orer to promote health, improve outcomes, and enhance patient safety. Unfortunately, current approaches to basic science research and clinical care are poorly integrated, yielding clinical decision-making processes that do not take advantage of up-to-date scientific knowledge in a manner supporting personalized healthcare. 

There are an increasing number of multi-modeling and integrative data analytics methods that can provide investigators and care givers with the tools needed in order to quickly generate hypotheses concerning the relationships between entities found in heterogeneous collections of scientific data — for example, exploring potential linkages been a gene, phenotype, and targeted therapeutics agents, thus enabling the “forward engineering” of treatment strategies based on knowledge generated via basic science studies. Ultimately, the goal of such methodologies is to accelerate the identification of high priority research questions that can make direct contributions to clinical practice. Given increasing concerns over the barriers to the timely translation of discoveries from the laboratory to the clinic or broader population settings, such high-throughput hypothesis generation, testing, and results dissemination is highly desirable. 

From a methodological standpoint, recent and significant advances in the state of systems biology and medicine have demonstrated that the ability to generate and reason across complex and scalar models is essential to the discovery of high-impact biologically and clinically actionable knowledge. At the most basic level, network-based multi-modeling across scales presents an elegant and computationally tractable approach to understanding and evaluating complex biomedical systems in order to discover the knowledge incumbent to such constructs. When taken as a whole, the theories and methods associated with the aforementioned ability to perform network-based and multi-modeling analyses across a variety of data scales are a prototypical example of the challenges and opportunities associated with the analysis and use of big data in biomedicine. 

In particular context, such theories and methods are examples of the types of techniques needed to generate actionable knowledge from diverse, large-scale, variable, and often high-velocity data sets wherein traditional data analytic methodologies are not capable of generating useful insights with downstream clinical actionability. It is this specific area that serves as the motivating for the MIDAs Training Program.

Program Leadership, Organization, and Decision Making Structure

MIDAs is led by Drs.  Kevin Coombes (Co-Chair, BMI Graduate Studies Coordinating Committee), Soledad Fernández (Vice Chair, Department of Biomedical Informatics), and Kun Huang (Division Director, Computational Biology and Bioinformatics). They are assisted by the department's education program manager who oversees the daily operations of the training program and formal oversight of the training program will be provided by the Graduate Studies Coordinating Committee (GSCC) co-chaired by Dr. Coombes and populated by faculty from OSU-BMI and affiliated training programs, including representatives from the OSU College of Medicine’s Biomedical Science Graduate Program, College of Engineering, and College of Public Health. 

The rationale for the preceding co-directorship model is based upon the complementary scholarly foci of Drs. Coombes, Fernández, and Huang, whose  laboratories  focus upon Biomedical Data Science and Bioinformatics, Clinical Research Informatics, Applied Biostatistics, and Translational Bioinformatics and Biomedical Data Science respectively.  In addition, Their complimentary expertise and extensive track records of mentoring and graduating trainees ensures a comprehensive and balanced approach to program leadership and trainee mentorship. In a similar manner, oversight from the BMI-GSSC will ensure that independent and knowledgeable input is provided relative to both strategic decision-making and program evaluation.