Floresence activated cell sorting (FACS) is a routine technique being carried out in protein engineering. This can also be a rate limiting step because of false positives. At present the softwares used for FACS are limited by the fact that they work in two dimensions.

       Recently Yu J.S. et.al.(2017) have developed an algoritm that is based on machine learning and uses positive and negative control populations. They have utilized a Bayesian  approach for prediction of number of sorting rounds.This approach has been found to be useful for reducing efforts in FACS.

     The source code written for implementing the algorithm can be obtained from the following website http://tyolab.northwestern.edu/tools

Reference:

Yu J.S.et al.(2017) Cell Sort:a support vector machine tool for optimizing fluoresence - activated cell sorting and reducing experimental effort. Bioinformatics 33(6) 909-916


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