Comparison of data discretization methods for cross platform transfer of gene-expression based tumor subtyping classifier

Segun Jung*, Yingtao Bi, Ramana V Davuluri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Molecular stratification of tumors is essential for developing personalized therapies. While patient stratification strategies have been successful, computational methods to accurately translate and integrate gene signatures across different high-throughput platforms (e.g., microarray, RNA-seq) are currently lacking. We performed comparative evaluation of different data discretization and feature selection methods combined with state-of-the-art machine learning algorithms to derive platform-independent and accurate multi-gene signatures for classification of the four known subtypes of glioblastoma. Our results show that integrative application of feature selection and data discretization is crucial for successful platform transition and higher prediction accuracy of the derived molecular classifiers.

Original languageEnglish (US)
Title of host publication2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479957866
DOIs
StatePublished - Jul 24 2014
Event2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014 - Miami, United States
Duration: Jun 2 2014Jun 4 2014

Publication series

Name2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014

Other

Other2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014
CountryUnited States
CityMiami
Period6/2/146/4/14

Keywords

  • cancer subtype prediction
  • data discretization
  • feature selection
  • isoform-level gene expression
  • platform transition

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Fingerprint Dive into the research topics of 'Comparison of data discretization methods for cross platform transfer of gene-expression based tumor subtyping classifier'. Together they form a unique fingerprint.

Cite this