{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008050109,0.00118382,0.0008993556,0.001973027,0.0003688573,0.0007083289,0.000830935,0.0008278622,0.002236551],"category_scores_gemma":[0.002553704,0.0002684409,0.0008441113,0.001336771,0.0002445977,0.001042329,0.0008318611,0.001097027,0.001518285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003800504,"about_ca_system_score_gemma":0.00115308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002501625,"about_ca_topic_score_gemma":0.003188737,"domain_scores_codex":[0.9994547,0.00006611695,0.00004770611,0.0001308464,0.0002248766,0.00007590451],"domain_scores_gemma":[0.9990098,0.0003620315,0.0001725977,0.0001012968,0.000273148,0.0000810643],"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":[0.003483415,0.0008028865,0.1808935,0.001395875,0.0005736505,0.001474972,0.0001835894,0.1785665,0.06319609,0.004391246,0.1059915,0.4590467],"study_design_scores_gemma":[0.0001174578,0.0003138287,0.01800701,0.00005653851,0.00009230789,0.0007354028,0.00007449871,0.9503956,0.01736876,0.003367583,0.009416455,0.00005447439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6036667,0.004583206,0.3242904,0.0006496697,0.0005222033,0.0003894313,0.03258342,0.02666277,0.006652275],"genre_scores_gemma":[0.7995594,0.001324638,0.1331407,0.0003136715,0.0001492924,0.0002494897,0.06082152,0.0004394386,0.004001956],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002501625,"threshold_uncertainty_score":0.007481992,"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."}}