{"id":"W3018300308","doi":"10.1101/2020.04.23.055004","title":"Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Identification (biology); Computational biology; Biclustering; Gene; Robustness (evolution); Biomarker discovery; Biology; Computer science; Cluster analysis; Genetics; Machine learning; Proteomics","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":[0.0007564999,0.0004963365,0.0005245894,0.002413656,0.0003193202,0.0006335953,0.0003639623,0.0002911041,0.002217384],"category_scores_gemma":[0.002270568,0.0002017861,0.0005515426,0.001442544,0.0004361408,0.0003692493,0.0009458466,0.0003592904,0.0004638437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004593182,"about_ca_system_score_gemma":0.0004340813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008771406,"about_ca_topic_score_gemma":0.00123632,"domain_scores_codex":[0.9995365,0.0001189767,0.00002459272,0.0001964096,0.00007941792,0.00004412582],"domain_scores_gemma":[0.9991114,0.0003753782,0.0001648029,0.0001432082,0.0001246861,0.00008049296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002966135,0.0002911513,0.219723,0.001742986,0.001426436,0.001386367,0.0009026824,0.09860975,0.3660199,0.01845131,0.01930235,0.269178],"study_design_scores_gemma":[0.0001738468,0.0003250322,0.2285713,0.0001563542,0.0004114253,0.001561734,0.0003443881,0.5639918,0.1175778,0.06435514,0.02242113,0.000110039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6999762,0.001832929,0.2737348,0.0006223487,0.00006834417,0.00022212,0.01789038,0.002825342,0.002827452],"genre_scores_gemma":[0.8792718,0.0003652737,0.1048576,0.0001826237,0.00004060214,0.0001956969,0.01328607,0.000253399,0.001546847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002413656,"threshold_uncertainty_score":0.007417858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00911139613276354,"score_gpt":0.205743359845457,"score_spread":0.1966319637126935,"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."}}