{"id":"W4394173294","doi":"10.6084/m9.figshare.19498050","title":"Additional file 6 of LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Landmark; Computer science; Selection (genetic algorithm); Throughput; Computational biology; DNA sequencing; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Biology; Genetics; DNA; Operating system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002704383,0.002435722,0.001624246,0.002409345,0.0008911152,0.002354947,0.002917221,0.002014253,0.3311999],"category_scores_gemma":[0.01119415,0.0007395842,0.001960235,0.003454654,0.0005113634,0.001158364,0.001452271,0.001907498,0.1143972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278818,"about_ca_system_score_gemma":0.002110004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008243018,"about_ca_topic_score_gemma":0.0209487,"domain_scores_codex":[0.998817,0.0002320698,0.0001330819,0.0004614387,0.0002108655,0.0001455708],"domain_scores_gemma":[0.9946326,0.003356101,0.0002571126,0.0007722826,0.0007502118,0.0002317582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002976654,0.00009530911,0.002423059,0.001723336,0.0001335186,0.00005447703,0.00003827503,0.001233467,0.0004265507,0.0004420489,0.9872593,0.005873084],"study_design_scores_gemma":[0.003102197,0.0002495694,0.01674617,0.000851017,0.0003330721,0.0003286957,0.0001700671,0.005794423,0.002675717,0.008220533,0.9613807,0.0001479023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001503351,0.00002741273,0.0002594366,0.00003645712,0.00001779979,0.00002431022,0.9986802,0.0006039448,0.000200044],"genre_scores_gemma":[0.001158515,0.00003226744,0.00160449,0.00007256673,0.00001293814,0.0002812813,0.9957689,0.000275859,0.0007931586],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3311999,"threshold_uncertainty_score":0.9539622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04394070293039091,"score_gpt":0.2590498996973343,"score_spread":0.2151091967669434,"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."}}