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-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 175
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.cmpb.2019.04.007
  • Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.223-231
  • Anahtar Kelimeler: RNA-Sequencing, Classification, Negative Binomial, Poisson, Linear discriminant analysis, SHRUNKEN CENTROIDS, SEQ, CLASSIFICATION, REVEALS, PACKAGE
  • İstanbul Üniversitesi Adresli: Evet

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.