{"id":"W4297394736","doi":"10.3389/fbinf.2022.954529","title":"Predicting liver cancer on epigenomics data using machine learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Epigenomics; Liver cancer; Epigenetics; DNA methylation; Computational biology; Biology; Feature selection; Cancer; Histone; Genome; Hepatocellular carcinoma; Computer science; Gene; Bioinformatics; Artificial intelligence; Genetics; Gene expression","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":[0.001102438,0.0005688437,0.000662717,0.002334103,0.0001733687,0.0005051729,0.0003861851,0.0006271038,0.0007775683],"category_scores_gemma":[0.003040242,0.0001617608,0.0007601854,0.001396886,0.0001500944,0.0003605031,0.0003685122,0.0005924733,0.0004666939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893494,"about_ca_system_score_gemma":0.0003496784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004295638,"about_ca_topic_score_gemma":0.005065328,"domain_scores_codex":[0.9996563,0.0001176762,0.00003242302,0.00009652845,0.00005205696,0.00004499053],"domain_scores_gemma":[0.9986224,0.000845643,0.0001630229,0.00009870914,0.0002210631,0.00004918136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001640309,0.0005926885,0.5168332,0.0005176194,0.0006750028,0.00120978,0.0001220765,0.2205479,0.01602449,0.0006672317,0.007696826,0.2334729],"study_design_scores_gemma":[0.00003580591,0.0002134222,0.1212973,0.00004843274,0.0001163511,0.000510516,0.0001056858,0.8652782,0.007768876,0.002032752,0.002562609,0.00003007383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926662,0.002482819,0.08470852,0.0008534002,0.00008839741,0.0001652053,0.01594753,0.001527448,0.001560503],"genre_scores_gemma":[0.9513021,0.0005671625,0.03166861,0.0001123322,0.00006075084,0.0001021523,0.01538508,0.00002881217,0.0007729793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004295638,"threshold_uncertainty_score":0.008541286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03194714082554689,"score_gpt":0.2715499766577785,"score_spread":0.2396028358322316,"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."}}