Evaluating machine learning methodologies for identification of cancer driver genes

• Published in: Scientific reports Vol. 11; no. 1; p. 12281
• Main Authors: Malebary, Sharaf J., Khan, Yaser Daanial
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/61; 631/67; Algorithms; Biomarkers, Tumor; Cancer; Computational Biology - methods; Correlation coefficient; Databases, Genetic; Genomics - methods; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; multidisciplinary; Neoplasms - genetics; Next-generation sequencing; Oncogenes; Reproducibility of Results; ROC Curve; Science; Science (multidisciplinary); Support Vector Machine; Web Browser
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-91656-8

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.