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February 26, 2019
4:10PM - 5:10PM
Location
Cockins Hall 240
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2019-02-26 17:10:00
2019-02-26 18:10:00
Topology, Geometry and Data Seminar - Pablo Camara
Title: Spectral Simplicial Theory for Feature Selection and Applications to Genomics
Speaker: Pablo Camara (University of Pennsylvania)
Abstract: We present a general framework for unsupervised feature selection based on the combinatorial Laplacian on simplicial complexes. Our framework generalizes ideas found in spectral network analysis to simplicial complex representations of the data. We illustrate the utility of this framework with some applications in genomics: the study of the gene expression programs that underlie continuous and dynamic cellular processes, the analysis of spatial patterns of transcription, and the identification of relevant genetic alterations in cancer.
Seminar URL: https://tgda.osu.edu/
Cockins Hall 240
OSU ASC Drupal 8
ascwebservices@osu.edu
America/New_York
public
Date Range
Add to Calendar
2019-02-26 16:10:00
2019-02-26 17:10:00
Topology, Geometry and Data Seminar - Pablo Camara
Title: Spectral Simplicial Theory for Feature Selection and Applications to Genomics
Speaker: Pablo Camara (University of Pennsylvania)
Abstract: We present a general framework for unsupervised feature selection based on the combinatorial Laplacian on simplicial complexes. Our framework generalizes ideas found in spectral network analysis to simplicial complex representations of the data. We illustrate the utility of this framework with some applications in genomics: the study of the gene expression programs that underlie continuous and dynamic cellular processes, the analysis of spatial patterns of transcription, and the identification of relevant genetic alterations in cancer.
Seminar URL: https://tgda.osu.edu/
Cockins Hall 240
Department of Mathematics
math@osu.edu
America/New_York
public
Description
Title: Spectral Simplicial Theory for Feature Selection and Applications to Genomics
Speaker: Pablo Camara (University of Pennsylvania)
Abstract: We present a general framework for unsupervised feature selection based on the combinatorial Laplacian on simplicial complexes. Our framework generalizes ideas found in spectral network analysis to simplicial complex representations of the data. We illustrate the utility of this framework with some applications in genomics: the study of the gene expression programs that underlie continuous and dynamic cellular processes, the analysis of spatial patterns of transcription, and the identification of relevant genetic alterations in cancer.
Seminar URL: https://tgda.osu.edu/
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