ESFinder is a supervised machine learning-based approach for exon skipping event identification from RNA-Seq data. It choose Random Forests as the classification algorithm. ESFinder is designed to train a model based on the common predictions from MATS, MISO and Splicing Index, and classify each candidate as a ture or false ES event. Moreover, ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification.
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IEEE Trans Nanobioscience. 2015 Apr 29.
Identification Exon Skipping Events From High-Throughput RNA Sequencing Data.
Bai Y, Ji S, Jiang Q, Wang Y.