{"id":"W2123832393","doi":"10.1110/ps.30301","title":"TM Finder: A prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales","year":2001,"lang":"en","type":"article","venue":"Protein Science","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Helicity; Transmembrane protein; Membrane protein; Phase (matter); Scale (ratio); Crystallography; Chemistry; Transmembrane domain; Sequence (biology); Globular protein; Membrane; Biological system; Physics; Biology; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008546332,0.0001427425,0.0001590954,0.00008028593,0.0002570651,0.0000455756,0.0002140479,0.0001063973,0.000005465837],"category_scores_gemma":[0.0001829387,0.000135161,0.00005584721,0.0002454035,0.0003234187,0.00003376059,0.00005448922,0.0000492404,7.544271e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000213916,"about_ca_system_score_gemma":0.00009538663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003448233,"about_ca_topic_score_gemma":0.000005731407,"domain_scores_codex":[0.9986639,0.00005377361,0.000261204,0.0004603202,0.0002759408,0.0002848395],"domain_scores_gemma":[0.9992838,0.00000572423,0.0001542465,0.0002301234,0.0002298956,0.00009624137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002370381,0.000358653,0.0001177201,0.0001005447,0.000008272687,5.65268e-7,0.00003791281,0.000006919622,0.9807053,0.00005432309,8.643109e-7,0.01837184],"study_design_scores_gemma":[0.001377128,0.001593102,0.0001229279,0.00008976725,0.000009730556,0.00001192465,0.00001644979,0.00730281,0.9883527,0.0006176908,0.0003659771,0.0001398268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392081,0.0001864592,0.0571562,0.00004743217,0.00004049344,0.003248772,0.00003592307,0.00002062403,0.00005595938],"genre_scores_gemma":[0.9766524,0.00002115219,0.02264198,0.00001658859,0.00004412271,0.0005190854,0.00001284363,0.00001257691,0.00007924264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03744427,"threshold_uncertainty_score":0.5511706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02048106366243656,"score_gpt":0.3044239047868701,"score_spread":0.2839428411244335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}