Current and future collaborations
Invitation to collaborate: Machine learning and statistical modeling for biomedical sciences
The increasing investment of NIH in the “Big Data to Knowledge,” (BD2K) initiative demonstrates the rising importance of computational biology and health informatics in life sciences and biological research. The use of sophisticated statistical and machine learning tools to analyze, catalog, and disseminate complex biological datasets, including genomic and proteomic research findings, imaging results, and electronic health records, becomes the condition sine qua non of a successful (i.e., fundable) research program. My interdisciplinary research integrating biology, biophysics, mathematics, computer science, and informatics opens several exciting collaborative opportunities. Specifically, I would like to share my expertise, engage in collaborations and, if appropriate, serve as a co-investigator, helping the fellow researchers in the following areas:
- Experimental design and planning, as well as post-hoc statistical data analysis
- Biological data processing (automated biodata curation, analysis, quality control, and feature extraction).
- Design of the data sharing and dissemination systems (following NIH and NSF guidance).
- Use of feature selection methodologies, modern dimensionality reduction approaches (manifold learning), and machine-learning classifiers for biomarker selection
- Development and validation of diagnostic tests.
- Computer-aided diagnostics, application of medical decision-making, and the use of computer-based decision support systems as a supplement to clinical judgment..
Page updated on 2022-01-17 13:31:29 -0500