Computational Biology meets Data Science (23w5152)


Gabriela Cohen-Freue (University of British Columbia)

Robert Gentleman (Harvard Medical School)

Maribel Hernández Rosales (CINVESTAV)

(University of Rochester)


With the advances of high-throughput technologies, genomics and related fields are hitting the thick data era. Many problems in biology cannot be studied with traditional and exact methods, and the development and application of tailored methods to analyze rich and large-scale biological datasets are still lagging behind. In addition, diverse layers of knowledge in genomics, such as transcriptomics, epigenomics, proteomics and metabolomics need to be integrated and studied jointly with multi-omics approaches to unfold the complex mechanisms of biological systems and organisms. Sparked with creative solutions from data science, computational and data scientists can contribute to the development and implementation of innovative solutions to extract, wrangle, visualize, analyze and store complex data resulting from high-throughput technologies with efficient, transparent and reproducible analytical pipelines.