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http://dx.doi.org/10.14801/jkiit.2018.16.12.25

A MA-plot-based Feature Selection by MRMR in SVM-RFE in RNA-Sequencing Data  

Kim, Chayoung (Div. of General Studies)
Publication Information
The Journal of Korean Institute of Information Technology / v.16, no.12, 2018 , pp. 25-30 More about this Journal
Abstract
It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.
Keywords
SVM-RFE; MA-plot-based methods; RNA-Seq; MRMR; DEG;
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