MLSeq: Machine learning interface for RNA-sequencing data


GÖKSÜLÜK D., ZARARSIZ G., KORKMAZ S., Eldem V. , Zararsiz G. E. , Ozcetin E., ...Daha Fazla

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, cilt.175, ss.223-231, 2019 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 175
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.cmpb.2019.04.007
  • Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Sayfa Sayıları: ss.223-231

Özet

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.