{"id":"W2804859730","doi":"10.1016/j.neucom.2018.05.047","title":"Deep learning for biological/clinical data","year":2018,"lang":"en","type":"article","venue":"Neurocomputing","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","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":[0.00123333,0.0006422973,0.0005870876,0.001383355,0.0002919317,0.001039034,0.0009476515,0.001126165,0.003389239],"category_scores_gemma":[0.004418842,0.0003199519,0.0007599178,0.001251945,0.000510352,0.001088745,0.001247481,0.001996373,0.00126656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008282733,"about_ca_system_score_gemma":0.001388447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005135317,"about_ca_topic_score_gemma":0.00738136,"domain_scores_codex":[0.9996301,0.00009536525,0.00003341071,0.0000895385,0.00009269626,0.00005894145],"domain_scores_gemma":[0.998782,0.0005832675,0.00009529579,0.0002333546,0.0002274825,0.00007860175],"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":[0.0002541885,0.0003220392,0.01026654,0.0005056356,0.0002215451,0.0002740461,0.0001121825,0.1346571,0.00842368,0.01966125,0.0203595,0.8049424],"study_design_scores_gemma":[0.00001567879,0.00006187624,0.00221092,0.0000959019,0.00004705654,0.0001491456,0.00004240663,0.9286211,0.004590026,0.05729228,0.006856811,0.00001685514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09039009,0.008143188,0.8750851,0.007227556,0.0005869059,0.0002228857,0.00613031,0.003713035,0.008500944],"genre_scores_gemma":[0.8108125,0.002750732,0.1694036,0.001239213,0.0003348136,0.0001946973,0.005138566,0.000134359,0.009991602],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005135317,"threshold_uncertainty_score":0.01133811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1820158952018076,"score_gpt":0.3901749811389061,"score_spread":0.2081590859370985,"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."}}