{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00351841,0.003135092,0.002538212,0.004187013,0.001629104,0.001642624,0.0031803,0.001789312,0.0143748],"category_scores_gemma":[0.007461053,0.001563171,0.002092101,0.002794965,0.0005035167,0.002240285,0.001873608,0.002539227,0.01254626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007341222,"about_ca_system_score_gemma":0.002077437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001717191,"about_ca_topic_score_gemma":0.002563073,"domain_scores_codex":[0.9978428,0.0005292494,0.0001975655,0.0007001153,0.0005789974,0.0001512812],"domain_scores_gemma":[0.9976268,0.0007933179,0.0003282154,0.0003222618,0.0007748314,0.0001545482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002479724,0.0005247114,0.01130279,0.00581944,0.001622002,0.002077817,0.0007532082,0.0505636,0.145596,0.01082719,0.3241031,0.4443304],"study_design_scores_gemma":[0.0004572737,0.0003095714,0.005466561,0.0003489137,0.0004230171,0.002041306,0.000270936,0.8037885,0.07735112,0.01995057,0.08924203,0.0003502085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02230956,0.00160963,0.8973244,0.0003164907,0.0003051792,0.0004484664,0.01065239,0.06370991,0.003323894],"genre_scores_gemma":[0.09893947,0.001043707,0.8439563,0.0002562944,0.000111751,0.001227612,0.04386408,0.006824323,0.003776472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0143748,"threshold_uncertainty_score":0.04808849,"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."}}