{"id":"W2949244194","doi":"10.1186/s12859-017-1553-8","title":"MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms","year":2017,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Diamantina Institute, University of Queensland; National Health and Medical Research Council; Medical Research Council; Australian Research Council; University of Queensland; Australian Cancer Research Foundation","keywords":"Multivariate statistics; Overfitting; Computer science; Data mining; Computational biology; DNA microarray; Artificial intelligence; Machine learning; Biology; Gene; Genetics","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":[0.01445871,0.00381228,0.00299573,0.005847692,0.001434209,0.003882742,0.003463447,0.001551664,0.006832032],"category_scores_gemma":[0.02825358,0.001383142,0.008179299,0.004450211,0.001700068,0.002341615,0.003730832,0.004890483,0.002457685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001517381,"about_ca_system_score_gemma":0.003532899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488306,"about_ca_topic_score_gemma":0.002484218,"domain_scores_codex":[0.9926617,0.00280361,0.0005614882,0.001884993,0.001724854,0.0003633317],"domain_scores_gemma":[0.9881819,0.006637173,0.001518723,0.00165128,0.001543191,0.000467745],"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":[0.00169242,0.0006324977,0.02910423,0.00258876,0.006285822,0.0008956125,0.001481525,0.1301791,0.08218141,0.02388501,0.02968605,0.6913875],"study_design_scores_gemma":[0.0001566789,0.0004705018,0.008408939,0.0001073858,0.0006501894,0.000588035,0.000193066,0.9228097,0.01909738,0.03103343,0.0162405,0.0002442525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006142902,0.0002329398,0.9844987,0.0001530693,0.00008291992,0.0002360406,0.0009015267,0.007407133,0.0003447678],"genre_scores_gemma":[0.07370075,0.00029648,0.9142784,0.0002507827,0.000209377,0.00196779,0.004771858,0.003608156,0.0009164515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01445871,"threshold_uncertainty_score":0.0764659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04099443180788442,"score_gpt":0.4106003870872167,"score_spread":0.3696059552793323,"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."}}