Project 6 involves developing statistical methodology that will be applicable to many of the very high dimensional datasets that are being gathered as part of the Conte Center. In particular, we will focus on models with single outcome variables and very high-dimensional predictors, e.g., using gene expression data to discriminate between suicide attempters and depressed non attempters, or using brain imaging data to predict a patient's response to treatment for depression. This methodology will employ powerful newly developing statistical concepts and tools including functional data analytic methods, machine learning techniques, and prescreening algorithms. Emphasis will be on developing models that can both achieve accurate predictions and provide stable interpretable models, allowing for a deeper understanding of the biological basis of suicidal behavior and mental illness. The project involves development of appropriate methodology, application both to existing datasets and to those that will be gathered as part of the Conte Center, and comparison among the various modeling strategies using both simulation studies and real data validations.

Public Health Relevance

In order to better understand the biological basis of suicidal behavior and mental illness, powerful methods for modeling data with very high dimensional data (e.g., brain imaging data, gene expression data) are required. This project is focused on developing appropriate statistical methodology that will allow for both accurate predictions and stable, interpretable models in situations arising as part of the Conte Center.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
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Special Emphasis Panel (ZMH1)
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New York State Psychiatric Institute
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