Affiliation:
1. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
Abstract
Diagnosing cancer and identifying the disease gene by using DNA microarray gene
expression data are the hot topics in current bioinformatics. This paper is devoted to the latest
development in cancer diagnosis and gene selection via statistical machine learning. A support
vector machine is firstly introduced for the binary cancer diagnosis. Then, 1-norm support vector
machine, doubly regularized support vector machine, adaptive huberized support vector machine
and other extensions are presented to improve the performance of gene selection. Lasso, elastic
net, partly adaptive elastic net, group lasso, sparse group lasso, adaptive sparse group lasso and
other sparse regression methods are also introduced for performing simultaneous binary cancer
classification and gene selection. In addition to introducing three strategies for reducing multiclass
to binary, methods of directly considering all classes of data in a learning model (multi_class
support vector, sparse multinomial regression, adaptive multinomial regression and so on) are
presented for performing multiple cancer diagnosis. Limitations and promising directions are also
discussed.
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
24 articles.
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