{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001875942,0.0001223523,0.00008315201,0.00006362831,0.00008172823,0.00006725955,0.0003264929,0.0001507266,0.0001561302],"category_scores_gemma":[0.00003288392,0.0001029091,0.00003785175,0.0001289239,0.0000266866,0.00002202448,0.0001838982,0.00007701163,0.0004693106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001882368,"about_ca_system_score_gemma":0.00007725375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003160138,"about_ca_topic_score_gemma":0.000001912614,"domain_scores_codex":[0.9991632,0.00001134864,0.0002367513,0.0002207168,0.0001933607,0.0001746409],"domain_scores_gemma":[0.9989937,0.000006141149,0.0001219455,0.0007437145,0.00006933933,0.00006516564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002821889,0.0001910023,0.007302582,0.0001216912,0.00009782984,5.39843e-7,0.0001558071,0.0001935907,0.2635881,0.0003061802,0.1262555,0.6015049],"study_design_scores_gemma":[0.001114042,0.0003744494,0.01419424,0.0000341441,0.00001174178,0.00001915882,0.0001666399,0.1281354,0.05608743,0.00003733136,0.7994695,0.0003558814],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8410672,0.0002992864,0.06802062,0.0003419378,0.002445295,0.001305288,0.0003271071,0.000168145,0.08602515],"genre_scores_gemma":[0.9522796,0.0002879225,0.02778741,0.00163162,0.000412284,0.00002844579,0.004361704,0.00005132473,0.01315971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.673214,"threshold_uncertainty_score":0.6032194,"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."}}