{"id":"W4386265420","doi":"10.1109/jbhi.2023.3309842","title":"SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining With Graph Modeling","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Artificial neural network; Pattern recognition (psychology); Graph; Machine learning; Data mining; Theoretical computer science","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":[0.0006484403,0.00111211,0.000986644,0.001009246,0.0003375463,0.0006281931,0.001937718,0.0008325197,0.001043373],"category_scores_gemma":[0.001897199,0.0004818451,0.001184856,0.001231361,0.0004918515,0.001576798,0.0009085792,0.001180176,0.0003738311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008143954,"about_ca_system_score_gemma":0.0009592098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009496956,"about_ca_topic_score_gemma":0.01150774,"domain_scores_codex":[0.9995489,0.00009107489,0.00002615857,0.0001648916,0.0001234042,0.00004543894],"domain_scores_gemma":[0.9995108,0.0001837331,0.00007081713,0.00006003544,0.0001471082,0.00002750613],"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":[0.0001368282,0.0001434499,0.003158456,0.0001490054,0.0001811819,0.0001321683,0.00009543726,0.624916,0.006001614,0.007823762,0.005486923,0.3517751],"study_design_scores_gemma":[0.000003492876,0.00001175973,0.0001233489,0.000002879424,0.000006277447,0.00001298166,0.000003512883,0.996834,0.0003911408,0.002289012,0.0003176902,0.000003857264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01393589,0.0002987549,0.9835972,0.0001493898,0.0000371476,0.00004720646,0.0002196082,0.001064981,0.0006498294],"genre_scores_gemma":[0.5658786,0.000790782,0.4227164,0.0004936125,0.00009020302,0.0005052917,0.002951353,0.000293683,0.006280024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009496956,"threshold_uncertainty_score":0.01888335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0537661517060797,"score_gpt":0.3243556052416787,"score_spread":0.270589453535599,"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."}}