Genomics

Dataset Information

0

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).

ORGANISM(S): Homo sapiens

PROVIDER: GSE77434 | GEO | 2016/02/02

SECONDARY ACCESSION(S): PRJNA310328

REPOSITORIES: GEO

Similar Datasets

2019-02-25 | GSE99573 | GEO
2019-03-27 | PXD012910 | Pride
2022-06-09 | ST002196 | MetabolomicsWorkbench
2020-09-16 | ST001527 | MetabolomicsWorkbench
2010-10-08 | GSE18495 | GEO
| E-MEXP-995 | biostudies-arrayexpress
2022-01-16 | PXD029956 | Pride
2022-02-23 | MTBLS4206 | MetaboLights
2010-05-05 | GSE21653 | GEO
| E-GEOD-21653 | biostudies-arrayexpress