@inproceedings{cb8fb32edce24bc18eb1fc76b6c554da,
title = "Comparison of data discretization methods for cross platform transfer of gene-expression based tumor subtyping classifier",
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.",
keywords = "cancer subtype prediction, data discretization, feature selection, isoform-level gene expression, platform transition",
author = "Segun Jung and Yingtao Bi and Davuluri, \{Ramana V.\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014 ; Conference date: 02-06-2014 Through 04-06-2014",
year = "2014",
month = jul,
day = "24",
doi = "10.1109/ICCABS.2014.6863918",
language = "English",
series = "2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014",
}