{"id":"W2928574665","doi":"10.1093/bioinformatics/btz223","title":"FusionLearn: a biomarker selection algorithm on cross-platform data","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data mining; Microarray databases; Software; Microarray analysis techniques; Biomarker discovery; R package; Selection (genetic algorithm); Machine learning; Proteomics; Biology; Gene","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":[0.005941782,0.002098569,0.001897792,0.003042289,0.0008435895,0.001311241,0.002573734,0.002585544,0.003345431],"category_scores_gemma":[0.01320521,0.000799555,0.002347359,0.00272614,0.0007172577,0.001881965,0.0025305,0.002482686,0.002169923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009650983,"about_ca_system_score_gemma":0.001719592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00353867,"about_ca_topic_score_gemma":0.003712535,"domain_scores_codex":[0.9977337,0.0006548999,0.0001936451,0.0007561431,0.0004916766,0.0001698445],"domain_scores_gemma":[0.9958936,0.002499384,0.000272939,0.0004546064,0.0007005245,0.0001789163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001842868,0.0005474681,0.01901474,0.0005876294,0.001313817,0.0007132647,0.0002029095,0.2362747,0.01392708,0.002847269,0.03926131,0.6834669],"study_design_scores_gemma":[0.0001340999,0.0001787131,0.001505735,0.00004310667,0.0001065817,0.0001881673,0.00003261495,0.9795725,0.005632422,0.008837423,0.003737838,0.00003096923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02561657,0.001065267,0.9476445,0.0006011006,0.0001793187,0.0002714453,0.002337337,0.0214257,0.0008588047],"genre_scores_gemma":[0.2318878,0.0003759169,0.7500656,0.0008407676,0.0001762867,0.0006445956,0.01204167,0.00166306,0.002304257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005941782,"threshold_uncertainty_score":0.03142351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03584298378024424,"score_gpt":0.3112971282966152,"score_spread":0.2754541445163709,"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."}}