{"id":"W3109879323","doi":"10.1145/3388440.3415992","title":"Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration","year":2020,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Convolutional neural network; Computer science; Artificial intelligence; Breast cancer; Feature (linguistics); Pattern recognition (psychology); Cancer; Convolution (computer science); Artificial neural network; Data integration; Machine learning; Data mining; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005034514,0.0007575896,0.0006413925,0.0006962944,0.0002658207,0.000488642,0.0008381084,0.000804217,0.001225381],"category_scores_gemma":[0.0008673576,0.0002808352,0.0007078808,0.00058715,0.0001886062,0.0005211718,0.0008256906,0.001098883,0.0003414719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000762997,"about_ca_system_score_gemma":0.001002423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01344736,"about_ca_topic_score_gemma":0.01534299,"domain_scores_codex":[0.9997961,0.00002992259,0.00001293493,0.00006760288,0.00003721733,0.0000562108],"domain_scores_gemma":[0.9997806,0.00007560245,0.00002850281,0.00001875488,0.00007131724,0.00002533461],"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":[0.0005022954,0.0005487564,0.03604871,0.00009880271,0.000300941,0.0003872059,0.00008604948,0.6675987,0.006405809,0.001919114,0.007285065,0.2788185],"study_design_scores_gemma":[0.000005477112,0.00003044371,0.001181545,0.000005168148,0.00001530209,0.00002104144,0.000007441054,0.9968393,0.0006734715,0.0009127363,0.0003029708,0.000005123159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4088101,0.002714391,0.5762988,0.001881033,0.0001506542,0.0001266554,0.003134956,0.003321569,0.003561849],"genre_scores_gemma":[0.9519176,0.0003674586,0.04180597,0.0002917132,0.00006140059,0.0001112212,0.002707437,0.00003826674,0.002698965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01344736,"threshold_uncertainty_score":0.02673817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04104945831964841,"score_gpt":0.2898994861582729,"score_spread":0.2488500278386245,"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."}}