{"id":"W4220816106","doi":"10.18280/isi.270115","title":"Enhanced CNN Model for Pancreatic Ductal Adenocarcinoma Classification Based on Proteomic Data","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pancreatic cancer; Pancreatic ductal adenocarcinoma; Computer science; Profiling (computer programming); Convolutional neural network; Proteomics; Deep learning; Artificial intelligence; Cancer; Bioinformatics; Medicine; Internal medicine; Biology","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":[0.0003471763,0.0007407615,0.0004909063,0.0005309765,0.0002085889,0.0005205949,0.0007140903,0.0006449469,0.001012255],"category_scores_gemma":[0.0006612911,0.0002481328,0.0007311002,0.0004608101,0.0002044872,0.0004890332,0.0003759913,0.0007582042,0.0003008558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008690693,"about_ca_system_score_gemma":0.0006871517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02437256,"about_ca_topic_score_gemma":0.01313549,"domain_scores_codex":[0.9998437,0.00001727653,0.00001104471,0.00004946923,0.00003556735,0.00004285192],"domain_scores_gemma":[0.9998674,0.00003493301,0.00001475938,0.00001155812,0.00006069575,0.00001058276],"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":[0.0004005362,0.0002176099,0.009048059,0.0001162534,0.0001658368,0.0004072956,0.00006272223,0.7113355,0.01644782,0.002267855,0.004281313,0.2552491],"study_design_scores_gemma":[0.000002196091,0.0000159392,0.0003943903,0.000003115521,0.000009183173,0.00001360255,0.000002214247,0.9982536,0.0009282964,0.0002180388,0.0001566708,0.000002771752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4173976,0.004585145,0.56222,0.001342509,0.0005487953,0.0001340415,0.0008999406,0.003172253,0.009699691],"genre_scores_gemma":[0.9591098,0.00106672,0.03201784,0.0001618202,0.0000705978,0.00006813921,0.0007121536,0.00004142963,0.006751527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02437256,"threshold_uncertainty_score":0.04846144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07835420863993277,"score_gpt":0.3261418656496637,"score_spread":0.2477876570097309,"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."}}