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

Content Provider	IEEE Xplore Digital Library
Author	Segun Jung Yingtao Bi Davuluri, R.V.
Copyright Year	2014
Description	Author affiliation: Dept. of Preventive Med., Northwestern Univ., Chicago, IL, USA (Segun Jung; Yingtao Bi; Davuluri, R.V.)
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.
Starting Page	1
Ending Page	2
File Size	204135
Page Count	2
File Format	PDF
ISBN	9781479957866
e-ISBN	9781479957880
DOI	10.1109/ICCABS.2014.6863918
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Radio frequency Accuracy Feature selection Cancer subtype prediction Genomics Platform transition Data discretization Isoform-level gene expression Gene expression Bioinformatics Cancer
Content Type	Text
Resource Type	Article

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