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Webinar: Biostatistics Seminar Series – “Computational Approaches to Multi-Omics Data Analysis in Precision Medicine”

September 9, 12:00 pm to 1:00 pm

“Computational Approaches to Multi-Omics Data Analysis in Precision Medicine” will be presented by Kamil Khanipov, PhD, Assistant Professor, Department of Pharmacology and Toxicology, The University of Texas Medical Branch.

Complex diseases, such as inflammatory bowel disease, arise as a result of interactions between the host’s microbial community (microbiomes), environmental factors (phenomics), and genetic factors (genomics). In the past, we have studied these biological problems using a single omics method. However, advances in sequencing and data generation have allowed the utilization of multiple platforms (multi-omics) to describe biological systems comprehensively. The identification of complex interactions between features (components) of the biological systems within these datasets is the key to further our understanding of them. Since features are simultaneously involved in multiple relations, not all interaction patterns are expected to exhibit a visually uninterrupted pattern. As a result, such relations cannot be detected using traditional correlation, mutual information, principal coordinate analysis, or covariation-based network inference approaches. They, however, may be identified and described as non-trivial 2-, 3- and higher-dimensional Boolean patterns. This presentation will share an approach for the identification of multidimensional Boolean patterns within multi-omics datasets (a combination of microbiome, gene expression, protein expression, and phenotypes). This talk will discuss the identification and visualization of multi-dimensional Boolean patterns in complex biological systems such as microbial communities as well as describe upcoming applications in precision medicine initiatives.

Watch via Zoom

Participants may also join by calling 929-205-6099, meeting ID 322 043 373, with passcode 167660.


September 9
12:00 pm to 1:00 pm
Event Category:


Department of Public Health Sciences

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