High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal...

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Bibliographic Details
Main Authors: Nguyen Phuoc Long, Seongoh Park, Nguyen Hoang Anh, Tran Diem Nghi, Sang Jun Yoon, Jeong Hill Park, Johan Lim, Sung Won Kwon
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:International Journal of Molecular Sciences
Subjects:
colorectal cancer
transcriptomics
diagnosis
biomarker
machine learning
variable selection
Online Access:http://www.mdpi.com/1422-0067/20/2/296

Internet

http://www.mdpi.com/1422-0067/20/2/296

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