{"doi":"10.1002/jcc.10411","title":"SVMtm: Support vector machines to predict transmembrane segments","abstract":"<jats:title>Abstract</jats:title><jats:p>A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of ∼99%. This method can be used to complement current transmembrane helix prediction methods and can be used for consensus analysis of entire proteomes. The predictor is located at <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://genet.imb.uq.edu.au/predictors/SVMtm\">http://genet.imb.uq.edu.au/predictors/SVMtm</jats:ext-link>. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 632–636, 2004</jats:p>","journal":"Journal of Computational Chemistry","year":2004,"id":588257,"datarank":4.354756101502794,"base_score":4.48863636973214,"endowment":4.48863636973214,"self_citation_contribution":0.6732954554598211,"citation_network_contribution":3.681460646042973,"self_endowment_contribution":0.6732954554598211,"citer_contribution":3.681460646042973,"corpus_percentile":null,"corpus_rank":null,"citation_count":88,"citer_count":84,"citers_with_citation_signal":76,"citers_with_endowment":76,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":19949,"name":"John S. Mattick","orcid":"0000-0002-7680-7527","position":1,"is_corresponding":false},{"id":146701,"name":"Rohan D. Teasdale","orcid":"0000-0001-7455-5269","position":2,"is_corresponding":false},{"id":668905,"name":"Zheng Yuan","orcid":"0000-0001-7179-2437","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"SVMtm: Support vector machines to predict transmembrane segments","abstract":"<jats:title>Abstract</jats:title><jats:p>A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of ∼99%. This method can be used to complement current transmembrane helix prediction methods and can be used for consensus analysis of entire proteomes. The predictor is located at <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://genet.imb.uq.edu.au/predictors/SVMtm\">http://genet.imb.uq.edu.au/predictors/SVMtm</jats:ext-link>. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 632–636, 2004</jats:p>","is_dataset_classified":null,"base_score":4.48863636973214,"endowment":4.48863636973214,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"14978706","pmcid":null,"openalex_id":"https://openalex.org/W2072471612","authors":[],"funders":[{"funder_name":"NIDDK NIH HHS","grant_id":"DK063400","title":null}],"total_grants":1,"fwci":2.3949,"citation_percentile":0.88389903,"influential_citations":12,"citation_trend":[{"year":2012,"count":7},{"year":2013,"count":6},{"year":2014,"count":2},{"year":2015,"count":2},{"year":2016,"count":1},{"year":2017,"count":6},{"year":2018,"count":3},{"year":2019,"count":3},{"year":2020,"count":1},{"year":2021,"count":1},{"year":2023,"count":2},{"year":2024,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"http://cbio.ensmp.fr/~jvert/svn/bibli/local/Yuan2004SVMtm.pdf","host_type":"GREEN"},{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjcc.10411","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.10411","host_type":"publisher"},{"url":"https://doi.org/10.1002/jcc.10411","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/14978706","host_type":"repository"},{"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.411.8984","host_type":""}],"fields_of_study":["Machine Learning in Bioinformatics","RNA and protein synthesis mechanisms","Genomics and Phylogenetic Studies","Computer Science","Medicine","Biology","Algorithms","Amino Acid Sequence","Databases, Protein","Membrane Proteins","Models, Molecular","Neural Networks, Computer","Protein Sorting Signals","Reproducibility of Results","Sequence Analysis, Protein","Software"],"mesh_terms":["Algorithms","Amino Acid Sequence","Membrane Proteins","Models, Molecular","Software","Reproducibility of Results","Neural Networks, Computer","Sequence Analysis, Protein","Protein Sorting Signals","Databases, Protein"],"keywords":["Transmembrane protein","Transmembrane domain","Support vector machine","Computer science","Pattern recognition (psychology)","Artificial intelligence","Computational biology","Biological system","Chemistry","Biology","Biochemistry","Membrane"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-20T00:07:02.752893Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}