{"id":"W4220730260","doi":"10.1186/s12859-022-04631-z","title":"LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Genome Canada","keywords":"Selection (genetic algorithm); Computational biology; Throughput; Computer science; DNA microarray; Landmark; DNA sequencing; Biology; Machine learning; Artificial intelligence; Genetics; DNA; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005627439,0.0001026944,0.0001203543,0.00006558673,0.0001170933,0.00002554959,0.0005337995,0.00004929801,0.000008986725],"category_scores_gemma":[0.00008862974,0.00008911388,0.00002873723,0.0002693197,0.00002573637,0.0000124428,0.0005870257,0.00008396769,0.000001868013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006595945,"about_ca_system_score_gemma":0.0002775102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004623235,"about_ca_topic_score_gemma":0.0009504445,"domain_scores_codex":[0.999099,0.00005727319,0.000312414,0.0001842862,0.0001548086,0.0001922269],"domain_scores_gemma":[0.9991049,0.00002541178,0.00009571227,0.0006871375,0.0000371465,0.00004971234],"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":[0.002005545,0.001331394,0.07064497,0.001071534,0.0005787797,0.000003770699,0.0106656,0.6583853,0.1275592,0.004210431,0.0847239,0.03881952],"study_design_scores_gemma":[0.001531437,0.000710577,0.002713527,0.00001231403,0.00004779756,0.00004515342,0.006658688,0.9356032,0.01307564,0.00007764527,0.03908549,0.0004385435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8723413,0.0003286059,0.12223,0.0001394906,0.0003484999,0.0008598693,0.0009091972,0.00001745316,0.002825577],"genre_scores_gemma":[0.8929916,0.00007295542,0.104399,0.0005083836,0.0000948709,0.00005008423,0.001833339,0.00001920165,0.00003056577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2772178,"threshold_uncertainty_score":0.3633959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03902402827336621,"score_gpt":0.2584703445125834,"score_spread":0.2194463162392172,"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."}}