{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ganakammal SR"],"funding":["The work of Emil Alexov was supported by grants from National Institutes of Health","NIGMS NIH HHS"],"pagination":["e8106"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6884988"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7"],"pubmed_abstract":["<h4>Background</h4>Genomics diagnostic tests are done for a wide spectrum of complex genetics conditions such as autism and cancer. The growth of technology has not only aided in successfully decoding the genetic variants that causes or trigger these disorders. However, interpretation of these variants is not a trivial task even at a level of distinguish pathogenic vs benign variants.<h4>Methods</h4>We used the clinically significant variants from ClinVar database to evaluate the performance of 14 most popular in-silico predictors using supervised learning methods. We implemented a feature selection and random forest classification algorithm to identify the best combination of predictors to evaluate the pathogenicity of a variant. Finally, we have also utilized this combination of predicto"],"journal":["PeerJ"],"pubmed_title":["Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants."],"pmcid":["PMC6884988"],"funding_grant_id":["R01GM093937 and R01125639","R01 GM093937"],"pubmed_authors":["Ganakammal SR","Alexov E"],"additional_accession":[]},"is_claimable":false,"name":"Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants.","description":"<h4>Background</h4>Genomics diagnostic tests are done for a wide spectrum of complex genetics conditions such as autism and cancer. The growth of technology has not only aided in successfully decoding the genetic variants that causes or trigger these disorders. However, interpretation of these variants is not a trivial task even at a level of distinguish pathogenic vs benign variants.<h4>Methods</h4>We used the clinically significant variants from ClinVar database to evaluate the performance of 14 most popular in-silico predictors using supervised learning methods. We implemented a feature selection and random forest classification algorithm to identify the best combination of predictors to evaluate the pathogenicity of a variant. Finally, we have also utilized this combination of predicto","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019","modification":"2025-04-22T17:35:37.266Z","creation":"2020-05-21T20:30:36Z"},"accession":"S-EPMC6884988","cross_references":{"pubmed":["31799076"],"doi":["10.7717/peerj.8106"]}}