{"id":"W2811144380","doi":"10.3390/molecules23071569","title":"Feature Selection via Swarm Intelligence for Determining Protein Essentiality","year":2018,"lang":"en","type":"article","venue":"Molecules","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Classifier (UML); Artificial intelligence; Swarm intelligence; Swarm behaviour; Selection (genetic algorithm); Feature (linguistics); Machine learning; Data mining; Particle swarm optimization","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":[0.001057787,0.0005904101,0.0008757906,0.001406569,0.0003130322,0.0005120226,0.0004596482,0.0004238914,0.0004634822],"category_scores_gemma":[0.002039958,0.0001715679,0.0007600427,0.0008974188,0.0003004768,0.0004817131,0.0003330837,0.0003584156,0.00009037117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003654457,"about_ca_system_score_gemma":0.0004532604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145002,"about_ca_topic_score_gemma":0.0008499908,"domain_scores_codex":[0.99958,0.0001077241,0.00004925744,0.00006767198,0.0001476663,0.00004774578],"domain_scores_gemma":[0.9992911,0.0003867671,0.00007028577,0.00004099979,0.000181589,0.0000292962],"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":[0.0003989928,0.0001865017,0.01434858,0.0002119913,0.0002319028,0.0003311512,0.0001799163,0.3665339,0.05085198,0.004650463,0.002485181,0.5595894],"study_design_scores_gemma":[0.00002111143,0.00008116514,0.002098295,0.000006055188,0.00003356096,0.00006801687,0.00001544463,0.9910433,0.004720579,0.001367967,0.0005330548,0.0000114707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1569043,0.0004540666,0.8407136,0.0001496814,0.00004450197,0.0000891212,0.0001100248,0.0005715684,0.0009631201],"genre_scores_gemma":[0.8224767,0.0002037535,0.1761252,0.00006870869,0.00003438075,0.000143157,0.0003093589,0.00003830182,0.0006003499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00145002,"threshold_uncertainty_score":0.005594194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008728061370248766,"score_gpt":0.2826654296705437,"score_spread":0.2739373683002949,"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."}}