{"id":"W2326199080","doi":"10.1371/journal.pone.0152964","title":"PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Specialized Research Fund for the Doctoral Program of Higher Education of China; National Science Foundation","keywords":"Computer science; Similarity (geometry); Artificial intelligence; Support vector machine; Ion channel; Identification (biology); Machine learning; Limit (mathematics); Sequence (biology); Pattern recognition (psychology); Data mining; Biology; Mathematics; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009947387,0.00008152177,0.0001062006,0.00002428485,0.00002402648,0.00000648442,0.00009117211,0.00007709649,0.00003035183],"category_scores_gemma":[0.0001973379,0.0000494009,0.00002146181,0.00003018579,0.00007033924,0.000003813181,0.00005427128,0.00003547162,0.00000695868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004695054,"about_ca_system_score_gemma":0.00003099038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002499684,"about_ca_topic_score_gemma":0.000001258182,"domain_scores_codex":[0.9995483,0.0000273667,0.0001339716,0.0001066803,0.00008114477,0.0001026061],"domain_scores_gemma":[0.9995877,0.00002465486,0.00009023395,0.0001894074,0.00006766582,0.00004032615],"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":[0.00004706389,0.00008151209,0.003760658,0.00009364334,0.00005767593,1.526142e-7,0.00005566566,0.00001325236,0.9947961,0.00002961134,0.0001062095,0.0009584565],"study_design_scores_gemma":[0.0004455663,0.0003443148,0.0007296642,0.0001785525,0.00001598142,9.472797e-7,0.00001146761,0.00244929,0.9948926,0.00008418084,0.0007530716,0.00009434642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974911,0.0001296027,0.001424708,0.0003856408,0.00003552533,0.0001120917,0.00004611583,0.00001389208,0.0003613343],"genre_scores_gemma":[0.9977505,0.0001330109,0.001520437,0.00009761874,0.0001180269,0.000007642505,0.0000309961,0.000009667515,0.0003321671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003030994,"threshold_uncertainty_score":0.201451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03070694186139493,"score_gpt":0.2384196092628684,"score_spread":0.2077126674014735,"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."}}