Genomics

Dataset Information

44

Functionally relevant prediction model for colorectal cancer


ABSTRACT: Filtered selection coupled with support vector machines generate functionally relevant prediction model for colorectal cancer. In this study, we built a model that uses Support Vector Machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300 and 500 genes most relevant to CRC using the Minimum-Redundancy–Maximum-Relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function and sigmoid). Overall design: We conducted a pair-wise comparison of Tumor vs Normal samples obtained from cancer patients. Array data was processed using Expression Console Patients detail for sample 052311 and 082812 are missing.

INSTRUMENT(S): [HuEx-1_0-st] Affymetrix Human Exon 1.0 ST Array [transcript (gene) version]

SUBMITTER: Musa Gabere  

PROVIDER: GSE77434 | GEO | 2016-02-02

SECONDARY ACCESSION(S): PRJNA310328

REPOSITORIES: GEO

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Publications

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer.

Gabere Musa Nur MN   Hussein Mohamed Aly MA   Aziz Mohammad Azhar MA  

OncoTargets and therapy 20160601


<h4>Purpose</h4>There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier.<h4>Methods</h4>In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 5  ...[more]

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