{"id":"W4286436802","doi":"10.18280/isi.270316","title":"Differential Evolution Model for Identification of Most Influenced Gene in Brest Cancer Data","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Computational biology; Microarray analysis techniques; Data mining; Gene; Identification (biology); Cluster (spacecraft); Benchmark (surveying); Computer science; DNA microarray; Medoid; Biology; Gene chip analysis; Gene expression; Genetics; Artificial intelligence; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001308067,0.0006220345,0.0008355321,0.001088844,0.0005082868,0.0006892167,0.001066797,0.0009964734,0.001311447],"category_scores_gemma":[0.003361371,0.0002653259,0.001136479,0.0009406584,0.0005189981,0.000579311,0.0004913271,0.001296642,0.0002505936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128035,"about_ca_system_score_gemma":0.0006586491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034676,"about_ca_topic_score_gemma":0.006741435,"domain_scores_codex":[0.999541,0.0001281002,0.0000291415,0.0001732968,0.00007322414,0.00005507763],"domain_scores_gemma":[0.9989067,0.0008120573,0.00007394204,0.0000426901,0.0001395528,0.00002503306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001587165,0.0000724554,0.007173527,0.00006580676,0.00007139796,0.0001699199,0.0001655431,0.9538228,0.003570094,0.005637913,0.0008663861,0.02822545],"study_design_scores_gemma":[0.000002878109,0.000009647066,0.0005281153,0.000002430066,0.000005551781,0.00002151165,0.000008520458,0.9975788,0.0003490241,0.001268055,0.0002215556,0.000003990154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2411591,0.0009260459,0.7530357,0.00097844,0.00007619826,0.0001420636,0.0009333072,0.0005001812,0.002248984],"genre_scores_gemma":[0.8889092,0.0004541647,0.1020868,0.000238444,0.00003933717,0.0003348085,0.001850974,0.00006354188,0.006022758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01034676,"threshold_uncertainty_score":0.02057308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503462670486827,"score_gpt":0.2802929010244348,"score_spread":0.2552582743195665,"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."}}