Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approache...

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Bibliographic Details
Main Authors: Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-06-01
Series:Frontiers in Molecular Biosciences
Subjects:
cancer driver mutations
machine learning classifiers
ensemble-based machine learning features
random forest
deep learning
convolutional neural networks
Online Access:https://www.frontiersin.org/article/10.3389/fmolb.2019.00044/full

Internet

https://www.frontiersin.org/article/10.3389/fmolb.2019.00044/full

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