{"id":"W2133898366","doi":"10.1109/bibm.2010.5706643","title":"Feature selection for graph kernels","year":2010,"lang":"en","type":"article","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cheminformatics; Computer science; Feature selection; Graph kernel; Graph; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Minimum redundancy feature selection; Kernel (algebra); Set (abstract data type); Data mining; Machine learning; Kernel method; Theoretical computer science; Mathematics; Support vector machine; Polynomial kernel; Bioinformatics","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":[0.001374018,0.0007605173,0.001033851,0.001813516,0.000416822,0.0007976518,0.0008199524,0.0007095815,0.001186917],"category_scores_gemma":[0.008597687,0.000243722,0.001138513,0.001685157,0.0004420156,0.001069816,0.0006719605,0.0008935209,0.00045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005333115,"about_ca_system_score_gemma":0.0006019544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001592335,"about_ca_topic_score_gemma":0.001207725,"domain_scores_codex":[0.9989166,0.0003930973,0.00007983782,0.000193142,0.0003144539,0.0001029171],"domain_scores_gemma":[0.9963451,0.002173136,0.0002494515,0.0004270356,0.0007209807,0.00008434695],"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":[0.0007082341,0.0002876235,0.004246208,0.0003087423,0.0001857408,0.0002936161,0.0001339648,0.2053059,0.03660993,0.01461091,0.007557906,0.7297512],"study_design_scores_gemma":[0.00003748915,0.0001012083,0.001678484,0.000008718363,0.00004115351,0.0001222603,0.00002706249,0.978124,0.00768725,0.01065181,0.001502329,0.00001828281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05600829,0.0003218026,0.9411092,0.0001903444,0.00004301173,0.0001212239,0.0002821474,0.00137817,0.0005458278],"genre_scores_gemma":[0.6384607,0.0002260312,0.358255,0.0001000731,0.00005833565,0.0003089368,0.001373713,0.0001731343,0.001044184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001813516,"threshold_uncertainty_score":0.007266581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378898803216126,"score_gpt":0.3054724442528697,"score_spread":0.2916834562207084,"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."}}