Literature DB >> 22306013

SVM-T-RFE: a novel gene selection algorithm for identifying metastasis-related genes in colorectal cancer using gene expression profiles.

Xiaobo Li1, Sihua Peng, Jian Chen, Bingjian Lü, Honghe Zhang, Maode Lai.   

Abstract

Although metastasis is the principal cause of death cause for colorectal cancer (CRC) patients, the molecular mechanisms underlying CRC metastasis are still not fully understood. In an attempt to identify metastasis-related genes in CRC, we obtained gene expression profiles of 55 early stage primary CRCs, 56 late stage primary CRCs, and 34 metastatic CRCs from the expression project in Oncology (http://www.intgen.org/expo/). We developed a novel gene selection algorithm (SVM-T-RFE), which extends support vector machine recursive feature elimination (SVM-RFE) algorithm by incorporating T-statistic. We achieved highest classification accuracy (100%) with smaller gene subsets (10 and 6, respectively), when classifying between early and late stage primary CRCs, as well as between metastatic CRCs and late stage primary CRCs. We also compared the performance of SVM-T-RFE and SVM-RFE gene selection algorithms on another large-scale CRC dataset and the five public microarray datasets. SVM-T-RFE bestowed SVM-RFE algorithm in identifying more differentially expressed genes, and achieving highest prediction accuracy using equal or smaller number of selected genes. A fraction of selected genes have been reported to be associated with CRC development or metastasis.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22306013     DOI: 10.1016/j.bbrc.2012.01.087

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  15 in total

1.  Prediction of O-glycosylation sites based on multi-scale composition of amino acids and feature selection.

Authors:  Yuan Chen; Wei Zhou; Haiyan Wang; Zheming Yuan
Journal:  Med Biol Eng Comput       Date:  2015-03-10       Impact factor: 2.602

2.  Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis.

Authors:  Xiaobo Li; Sihua Peng
Journal:  Chin J Cancer Res       Date:  2013-12       Impact factor: 5.087

3.  A classification framework applied to cancer gene expression profiles.

Authors:  Hussein Hijazi; Christina Chan
Journal:  J Healthc Eng       Date:  2013       Impact factor: 2.682

4.  Serum biomarker-based osteoporosis risk prediction and the systemic effects of Trifolium pratense ethanolic extract in a postmenopausal model.

Authors:  Yixian Quah; Jireh Chan Yi-Le; Na-Hye Park; Yuan Yee Lee; Eon-Bee Lee; Seung-Hee Jang; Min-Jeong Kim; Man Hee Rhee; Seung-Jin Lee; Seung-Chun Park
Journal:  Chin Med       Date:  2022-06-14       Impact factor: 4.546

5.  FANCF hypomethylation is associated with colorectal cancer in Han Chinese.

Authors:  Hang Yu; Ranran Pan; Tong Gao; Dongping Wu; Jieer Ying; Shiwei Duan
Journal:  Turk J Gastroenterol       Date:  2020-08       Impact factor: 1.852

6.  Binary matrix shuffling filter for feature selection in neuronal morphology classification.

Authors:  Congwei Sun; Zhijun Dai; Hongyan Zhang; Lanzhi Li; Zheming Yuan
Journal:  Comput Math Methods Med       Date:  2015-03-29       Impact factor: 2.238

7.  Informative gene selection and direct classification of tumor based on Chi-square test of pairwise gene interactions.

Authors:  Hongyan Zhang; Lanzhi Li; Chao Luo; Congwei Sun; Yuan Chen; Zhijun Dai; Zheming Yuan
Journal:  Biomed Res Int       Date:  2014-07-23       Impact factor: 3.411

8.  Sparse feature selection for classification and prediction of metastasis in endometrial cancer.

Authors:  Mehmet Eren Ahsen; Todd P Boren; Nitin K Singh; Burook Misganaw; David G Mutch; Kathleen N Moore; Floor J Backes; Carolyn K McCourt; Jayanthi S Lea; David S Miller; Michael A White; Mathukumalli Vidyasagar
Journal:  BMC Genomics       Date:  2017-03-27       Impact factor: 3.969

9.  Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

Authors:  Xiaohui Lin; Chao Li; Yanhui Zhang; Benzhe Su; Meng Fan; Hai Wei
Journal:  Molecules       Date:  2017-12-26       Impact factor: 4.411

Review 10.  Machine learning methods in the computational biology of cancer.

Authors:  M Vidyasagar
Journal:  Proc Math Phys Eng Sci       Date:  2014-07-08       Impact factor: 2.704

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