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An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

Crossref DOI link: https://doi.org/10.1186/s13040-024-00420-x

Published Online: 2025-01-20

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Das, Subrata

Patel, Vatsal

Chakravarty, Shouvik

Ghosh, Arnab

Mukhopadhyay, Anirban

Biswas, Nidhan K. https://orcid.org/0000-0001-7960-3290
Funding

Funding for this research was provided by:

Department of Science and Technology, India (CRG/2022/007730)

Indian Council of Medical Research, India (BMI/12(06)/2022 and IRIS ID No. 2021-14057)
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Text and Data Mining valid from 2025-01-20

Version of Record valid from 2025-01-20
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Article History

Received: 4 October 2024

Accepted: 26 December 2024

First Online: 20 January 2025

Declarations

:

: All the authors approved the manuscript for publication.

: The authors declare no competing interests.

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