CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort | Zendy

Jessica S. Yu | Zendy; Dante Pertusi | Zendy; Adebola Adeniran | Zendy; Keith E. J. Tyo | Zendy

Open Access

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort

Author(s) -

Jessica S. Yu,

Dante Pertusi,

Adebola Adeniran,

Keith E. J. Tyo

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw710

Subject(s) - sorting , computer science , software , sorting algorithm , support vector machine , source code , population , bayesian probability , cell sorting , data mining , machine learning , artificial intelligence , algorithm , programming language , biology , genetics , demography , sociology , cell

High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required.

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