{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Petti S"],"funding":["NSF-Simons Center for the Mathematical and Statistical Analysis of Biology","NHGRI NIH HHS","National Human Genome Research Institute"],"pagination":["e1009492"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8929697"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(3)"],"pubmed_abstract":["Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets."],"journal":["PLoS computational biology"],"pubmed_title":["Constructing benchmark test sets for biological sequence analysis using independent set algorithms."],"pmcid":["PMC8929697"],"funding_grant_id":["R01 HG009116","R01-HG009116","1764269"],"pubmed_authors":["Eddy SR","Petti S"],"additional_accession":[]},"is_claimable":false,"name":"Constructing benchmark test sets for biological sequence analysis using independent set algorithms.","description":"Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-19T08:41:42.457Z","creation":"2025-02-19T05:05:14.038Z"},"accession":"S-EPMC8929697","cross_references":{"pubmed":["35255082"],"doi":["10.1371/journal.pcbi.1009492"]}}