{"id":"W2023720082","doi":"10.5555/3191835.3191980","title":"Improving energetic feature selection to classify protein: protein interactions","year":2014,"lang":"en","type":"article","venue":"Advances in Social Networks Analysis and Mining","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Transient (computer programming); Kernel (algebra); Support vector machine; Computer science; Feature selection; Protein–protein interaction; Set (abstract data type); Artificial intelligence; Stability (learning theory); Machine learning; Biological system; Pattern recognition (psychology); Chemistry; Mathematics; Biology","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":[0.001541134,0.0008893247,0.001102406,0.002138698,0.000420262,0.000795324,0.0006615124,0.0007603553,0.001013107],"category_scores_gemma":[0.002636849,0.000134394,0.0007586559,0.001374958,0.000194208,0.0007614818,0.0005098561,0.0005723935,0.0006461971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003275231,"about_ca_system_score_gemma":0.0003863462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146306,"about_ca_topic_score_gemma":0.001236347,"domain_scores_codex":[0.9992168,0.0002314255,0.00008287938,0.0001428902,0.0001889007,0.0001370781],"domain_scores_gemma":[0.9984317,0.0008241874,0.0001715043,0.0001045873,0.0003804975,0.00008742758],"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":[0.001489733,0.001267167,0.1014841,0.0002313713,0.000479365,0.0004259496,0.0001576304,0.09844352,0.02673865,0.001316303,0.01167404,0.7562923],"study_design_scores_gemma":[0.00003711049,0.0001815507,0.02100373,0.00001504174,0.00006350726,0.0001585999,0.00006106245,0.9701801,0.005822326,0.001207341,0.001249668,0.00002005427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8048396,0.0020338,0.187067,0.0004091098,0.0001595074,0.0001272529,0.001234799,0.001726989,0.002402052],"genre_scores_gemma":[0.9730546,0.0001680691,0.02396605,0.00004550326,0.00007960234,0.00005653799,0.001707257,0.00002887372,0.0008935038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002146306,"threshold_uncertainty_score":0.008150399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003097970861662444,"score_gpt":0.2515122936968413,"score_spread":0.2484143228351789,"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."}}