{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007296127,0.0001205465,0.0002185422,0.0001002001,0.0002166856,0.00004125072,0.0001566941,0.0001599627,7.550372e-7],"category_scores_gemma":[0.00003037127,0.00008195319,0.00003093189,0.0002234306,0.000057391,0.00002223371,0.00006945169,0.0002440426,1.216049e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008293614,"about_ca_system_score_gemma":0.0001932747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001370468,"about_ca_topic_score_gemma":0.000003147064,"domain_scores_codex":[0.9989545,0.00002178291,0.0004226854,0.0001161924,0.0002235581,0.0002612276],"domain_scores_gemma":[0.9990993,0.00001994166,0.0003546457,0.00013634,0.0001146147,0.0002751737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002669122,0.0002329954,0.004643104,0.003596969,0.0006987668,0.00001622796,0.007840717,0.02242677,0.08415698,0.00004427847,0.582015,0.291659],"study_design_scores_gemma":[0.002917481,0.001307935,0.0003686952,0.0003513245,0.00004514953,0.0002888914,0.002604108,0.8735153,0.000276772,0.00002688023,0.1180861,0.0002113635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7701638,0.004164461,0.2122256,0.01242723,0.0005536723,0.0003325424,0.00009139167,0.00002303266,0.00001821764],"genre_scores_gemma":[0.7601701,0.01859025,0.2099921,0.0054408,0.003536127,0.00002212518,0.001809577,0.00006558488,0.0003733792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8510886,"threshold_uncertainty_score":0.3341954,"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."}}