{"id":"W2120257389","doi":"10.1155/2013/409658","title":"An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function","year":2013,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Cancer Foundation; Allard Foundation; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Canadian Cancer Society; Ministry of Advanced Education, Government of Alberta","keywords":"Support vector machine; Computer science; Protein function; Sensitivity (control systems); Set (abstract data type); Function (biology); Computational biology; Test set; Annotation; Ligand (biochemistry); Sequence (biology); Artificial intelligence; Binding site; Data mining; Machine learning; Pattern recognition (psychology); Chemistry; Biology; Biochemistry; Engineering; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008447144,0.0007586133,0.0009614154,0.001272248,0.0002827489,0.0005511002,0.0008736149,0.0009213603,0.0008130237],"category_scores_gemma":[0.001685556,0.0003001867,0.0005641539,0.0006893425,0.0003337353,0.001444276,0.0007691585,0.001009059,0.0003634097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000470376,"about_ca_system_score_gemma":0.0008494913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028957,"about_ca_topic_score_gemma":0.001487624,"domain_scores_codex":[0.9993579,0.000135821,0.00004261824,0.00009162282,0.0003051459,0.00006695768],"domain_scores_gemma":[0.999464,0.0001788093,0.00006332171,0.00004337398,0.000207551,0.00004302939],"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":[0.0003866107,0.0002611797,0.01192017,0.0002796401,0.0001002907,0.0002134077,0.0001011461,0.1359961,0.0785491,0.01056998,0.00480228,0.7568201],"study_design_scores_gemma":[0.00001410736,0.00005797024,0.001094609,0.000005929413,0.00001106543,0.0001064537,0.00001243242,0.9912922,0.004971161,0.001711102,0.0007104515,0.00001248674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02684089,0.000431073,0.9711863,0.0001069782,0.0000349488,0.00005221772,0.00007198641,0.0008257857,0.000449864],"genre_scores_gemma":[0.5298262,0.000648874,0.4665692,0.0001411134,0.00005735062,0.0001763058,0.0004728677,0.00007288556,0.002035141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002028957,"threshold_uncertainty_score":0.004467368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444966576672456,"score_gpt":0.4552948592282635,"score_spread":0.3107982015610179,"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."}}