MLSeq: Machine learning interface for RNA-sequencing data

GÖKSÜLÜK D., ZARARSIZ G., Korkmaz S., Eldem V., Zararsiz G., Ozcetin E., ...More

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.175, pp.223-231, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 175
  • Publication Date: 2019
  • Doi Number: 10.1016/j.cmpb.2019.04.007
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.223-231
  • Keywords: RNA-Sequencing, Classification, Negative Binomial, Poisson, Linear discriminant analysis
  • Erciyes University Affiliated: Yes

Abstract

Background and Objective: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network.