EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.

BMC Medical Genomics
Leila MirsadeghiKaveh Kavousi

Abstract

Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited. In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI. This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some e...Continue Reading

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Citations

Aug 7, 2021·Molecular Diversity·Akanksha Rajput, Manoj Kumar

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Software Mentioned

Needle Plot
OncoPrint
SIFT
ReactomeFIVIz
OncodriveFM
PolyPhen2
Illumina Genome Analyzer II
EARN
scikit
NLSVM

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