Joint CMU-Pitt Ph.D. Program in Computational Biology Seminar

  • Remote Access - Zoom
  • Virtual Presentation - ET
  • SANDRA SAFO
  • Assistant Professor of Biostatistics
  • Division of Biostatistics
  • University of Minnesota
Seminars

SIDA and SIDANet: New Discriminant Analysis Methods for Multi-class, Multi-view Structured Data

Classification methods that leverage the strengths of data from multiple sources (multi-view data) simultaneously have enormous potential to yield more powerful findings than two step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA) and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multi-view data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low- vs high-risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multi-view data and to perform classification.

Sandra E. Safo is Assistant Professor of Biostatistics at the Division of Biostatistics, University of Minnesota. My primary research focuses on developing and applying advanced statistical methods and computational tools for big, biomedical data to advance clinical translational research and precision medicine. I have developed both data-driven and knowledge-driven statistical methods for integrative analysis of “omics” data. My current applied research, which is funded through the University of Minnesota’s Clinical and Translational Science Institute’s KL2 scholar program seeks to use state-of-the-art statistical and machine learning methods (including methods developed by my research group) to identify novel nontraditional biomarkers that are associated with cardiovascular diseases in persons living with HIV.

Faculty Host:  Omer Acar (Pitt)

Zoom Participation. See announcement.

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