Machine learning for biomarker identification in cancer research - developments toward its clinical application

Personalized Medicine
Zeenia Jagga, Dinesh Gupta

Abstract

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

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Citations

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Methods Mentioned

BETA
nuclear magnetic resonance
PCA
PCR

Software Mentioned

EllipsoidFN
ImageMiner
MATLAB
PubMed
R
EvoTSP
RDRToolbox
iPCC
PubMed Central
GitHub Open source cancer

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