{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001720665,0.0001330212,0.00009154632,0.00003146713,0.0001499063,0.00004013009,0.0001652059,0.0001475812,0.00001056669],"category_scores_gemma":[0.0002011195,0.0001276198,0.00006566009,0.000077711,0.0000875222,0.000004270761,0.00007611624,0.00009115652,0.00001569731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001074786,"about_ca_system_score_gemma":0.00003433777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007119784,"about_ca_topic_score_gemma":0.00003524639,"domain_scores_codex":[0.9992904,0.00003655603,0.0001424655,0.0002231904,0.00009251129,0.0002148458],"domain_scores_gemma":[0.9994959,0.000007418261,0.0001010224,0.0002038848,0.0001456459,0.00004618961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001019592,0.00003180317,0.0006522688,0.00008685704,0.00004022063,5.529927e-7,0.0001202093,0.0001377472,0.9733598,0.0002818887,0.001959624,0.02322706],"study_design_scores_gemma":[0.0001205038,0.0004712605,0.0002758721,0.00001760092,0.00001179363,0.00001575728,0.00002040005,0.01673143,0.9441879,0.0002466206,0.03772287,0.0001779315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3327941,0.00003450653,0.6657455,0.000185475,0.0001075083,0.0003049191,0.000005679821,0.00003062004,0.0007916846],"genre_scores_gemma":[0.9286037,0.000002824019,0.06973593,0.0002382114,0.0003971013,0.00005780203,0.00007645817,0.00002218455,0.0008657826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5960096,"threshold_uncertainty_score":0.5204185,"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."}}