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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2021-06-01
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doaj-4c75836bc7dc4a73b34571eaaf9720882021-06-13T11:38:05ZengNature Publishing GroupScientific Reports2045-23222021-06-0111111310.1038/s41598-021-91656-8Evaluating machine learning methodologies for identification of cancer driver genesSharaf J. Malebary0Yaser Daanial Khan1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Computer Science, School of Systems and Technology, University of Management and TechnologyAbstract 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.https://doi.org/10.1038/s41598-021-91656-8 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sharaf J. Malebary Yaser Daanial Khan |
spellingShingle |
Sharaf J. Malebary Yaser Daanial Khan Evaluating machine learning methodologies for identification of cancer driver genes Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-06-01 |
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. |
url |
https://doi.org/10.1038/s41598-021-91656-8 |
work_keys_str_mv |
AT sharafjmalebary evaluatingmachinelearningmethodologiesforidentificationofcancerdrivergenes AT yaserdaanialkhan evaluatingmachinelearningmethodologiesforidentificationofcancerdrivergenes |
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