Evaluating machine learning methodologies for identification of cancer driver genes

Abstract Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge da...

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Autores principales:	Sharaf J. Malebary, Yaser Daanial Khan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4c75836bc7dc4a73b34571eaaf972088
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spelling	oai:doaj.org-article:4c75836bc7dc4a73b34571eaaf9720882021-12-02T17:52:32ZEvaluating machine learning methodologies for identification of cancer driver genes10.1038/s41598-021-91656-82045-2322https://doaj.org/article/4c75836bc7dc4a73b34571eaaf9720882021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91656-8https://doaj.org/toc/2045-2322Abstract Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.Sharaf J. MalebaryYaser Daanial KhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sharaf J. Malebary Yaser Daanial Khan Evaluating machine learning methodologies for identification of cancer driver genes
description	Abstract Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.
format	article
author	Sharaf J. Malebary Yaser Daanial Khan
author_facet	Sharaf J. Malebary Yaser Daanial Khan
author_sort	Sharaf J. Malebary
title	Evaluating machine learning methodologies for identification of cancer driver genes
title_short	Evaluating machine learning methodologies for identification of cancer driver genes
title_full	Evaluating machine learning methodologies for identification of cancer driver genes
title_fullStr	Evaluating machine learning methodologies for identification of cancer driver genes
title_full_unstemmed	Evaluating machine learning methodologies for identification of cancer driver genes
title_sort	evaluating machine learning methodologies for identification of cancer driver genes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4c75836bc7dc4a73b34571eaaf972088
work_keys_str_mv	AT sharafjmalebary evaluatingmachinelearningmethodologiesforidentificationofcancerdrivergenes AT yaserdaanialkhan evaluatingmachinelearningmethodologiesforidentificationofcancerdrivergenes
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Evaluating machine learning methodologies for identification of cancer driver genes

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