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Kevin Rychel

Researcher at University of California, San Diego

Publications -  22
Citations -  240

Kevin Rychel is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Biology & Transcriptome. The author has an hindex of 4, co-authored 10 publications receiving 43 citations.

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iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

TL;DR: iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA, is presented, enhancing research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels.
Journal ArticleDOI

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

TL;DR: The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing, and facilitates quantification of population-level sporulation states.
Posted ContentDOI

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

TL;DR: iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA, is presented, enhancing research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels.
Posted ContentDOI

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

TL;DR: The TRN structure and its condition-dependent activity uncover novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing, and facilitates quantification of population-level sporulation states.
Journal ArticleDOI

Emerging themes and unifying concepts underlying cell behavior regulation by the pericellular space.

TL;DR: Findings lend deeper mechanistic insight into how biomaterials can be designed to fine-tune outcomes like differentiation, migration, and collective morphogenesis.