Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the...

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Bibliographic Details
Main Authors: Ionut Dragomir, Adnan Akbar, John W. Cassidy, Nirmesh Patel, Harry W. Clifford, Gianmarco Contino
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Cancers
Subjects:
pan-cancer
classification
decision tree
driver mutation
extreme gradient boosting
k-nearest neighbours
Online Access:https://www.mdpi.com/2072-6694/13/11/2779

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https://www.mdpi.com/2072-6694/13/11/2779

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