{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Das S"],"funding":["Department of Science and Technology, India","Indian Council of Medical Research, India"],"pagination":["7"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11744934"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(1)"],"pubmed_abstract":["<h4>Background and objective</h4>Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).<h4>Methods</h4>The study used a dataset from 502 HNSC patients, classifying mutations based on 299 "],"journal":["BioData mining"],"pubmed_title":["An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer."],"pmcid":["PMC11744934"],"funding_grant_id":["BMI/12(06)/2022 and IRIS ID No. 2021-14057","CRG/2022/007730"],"pubmed_authors":["Ghosh A","Das S","Biswas NK","Patel V","Mukhopadhyay A","Chakravarty S"],"additional_accession":[]},"is_claimable":false,"name":"An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer.","description":"<h4>Background and objective</h4>Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).<h4>Methods</h4>The study used a dataset from 502 HNSC patients, classifying mutations based on 299 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2025-04-05T09:31:46.702Z","creation":"2025-04-05T09:31:46.702Z"},"accession":"S-EPMC11744934","cross_references":{"pubmed":["39833905"],"doi":["10.1186/s13040-024-00420-x"]}}