博文
Prediction of Core Cancer Gene using MTC Framework
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@ARTICLE{GXFF13,
author = {Gao, Shan and Xu, Shuo and Fang, Yaping and Fang, Jianwen},
title = {Prediction of Core Cancer Gene using Multi-task Classification Framework},
journal = {Journal of Theoretical Biology},
year = {2013},
volume = {317},
pages = {62--70},
abstract = {Cancer is deemed as a highly heterogeneous disease specific to cell
type and tissue origin. All cancers, however, share a common pathogenesis.
Therefore, it is widely believed that cancers may share common mechanisms.
In this study, we introduce a novel strategy based on multi-tasking
learning methods to predict core cancer genes shared by multiple
cancers in the hope of elucidating common cancer mechanisms. Our
strategy uses two multi-tasking learning algorithms, one for feature
selection and the other for validation of selected features. The
combined use of two methods results in more robust classifiers and
reliable selected features. The top 73 significant features, mapped
to 72 genes, are selected as core cancer genes. The effectiveness
of the 73 features is further demonstrated in a blind test conducted
on an independent test data. The biological significance of these
genes is evaluated using systems biology analyses. Extensive functional,
pathway and network analysis confirms findings in previous studies
and brings new insights into common cancer mechanisms. Our strategy
can be used as a general method to find important genes from large
gene expression datasets on the genomic level. The selected genes
can be used to predict cancers.},
keywords = {Multi-task Learning sep Classification sep Core Cancer Genes sep
Gene Differential Expression sep Microarray Data},
}
全文见：GXFF13.pdf
https://blog.sciencenet.cn/blog-611051-664305.html
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