{"id":"W2112905569","doi":"10.1093/nar/gkh380","title":"ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability","year":2004,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":202,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Topology (electrical circuits); Network topology; Biology; Computer science; Reliability (semiconductor); Process (computing); Algorithm; Data mining; Mathematics; Physics; Computer network; Combinatorics; Power (physics); Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002064243,0.0001826263,0.0002425304,0.0001082271,0.0004544988,0.00002544886,0.0003101729,0.000335302,0.00002911014],"category_scores_gemma":[0.0005676163,0.0001554949,0.00007928536,0.0002034274,0.000429908,0.000009150912,0.0001374564,0.000479684,0.000004678217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007805155,"about_ca_system_score_gemma":0.0003169172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002366235,"about_ca_topic_score_gemma":0.00005277143,"domain_scores_codex":[0.9979186,0.0002801615,0.0003410568,0.0004733179,0.0003933409,0.0005935032],"domain_scores_gemma":[0.9985613,0.0001454178,0.00006174194,0.0005844526,0.0005076387,0.000139433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007710901,0.0006384059,0.001697804,0.0008144985,0.0004648911,0.00001777033,0.004774892,0.498124,0.4415337,0.02815425,0.003028321,0.01304061],"study_design_scores_gemma":[0.0261198,0.02690491,0.002594897,0.0002479812,0.0001766042,0.0005306987,0.002766345,0.5006266,0.3286997,0.04297597,0.06680374,0.001552699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7536829,0.00007286957,0.2403818,0.002150845,0.0000776158,0.0009132727,0.00009385879,0.00006065433,0.002566229],"genre_scores_gemma":[0.7986664,0.00003523341,0.2003247,0.0001250122,0.0001355018,0.0001154359,0.0001484931,0.00003644684,0.0004127981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.112834,"threshold_uncertainty_score":0.6340898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03156165825524426,"score_gpt":0.349017576479136,"score_spread":0.3174559182238917,"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."}}