{"id":"W1554825545","doi":"10.1186/1471-2105-7-216","title":"A factor analysis model for functional genomics","year":2006,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Functional genomics; Computer science; Genomics; Factor (programming language); Gene ontology; Expression (computer science); DNA microarray; Computational biology; Structural genomics; Data mining; Biological data; Computation; Genome; Artificial intelligence; Machine learning; Bioinformatics; Gene; Biology; Algorithm; Genetics; Gene expression","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":[0.0001449786,0.0001964099,0.0002078767,0.0001124596,0.0001342939,0.0000693492,0.0001756053,0.0002095781,0.00001155808],"category_scores_gemma":[0.00001636601,0.0001822268,0.0003417851,0.0001670742,0.00004989925,0.00001120988,0.00007785136,0.00006342703,0.00001669752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003124445,"about_ca_system_score_gemma":0.0001468089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000462555,"about_ca_topic_score_gemma":0.0001163028,"domain_scores_codex":[0.9988694,0.000006306884,0.0005230814,0.0001510443,0.000128447,0.0003216749],"domain_scores_gemma":[0.9992242,0.00001832318,0.0002052234,0.0003657061,0.0001124711,0.00007410888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001587266,0.00008324286,0.003558067,0.0001240882,0.0004145155,9.395951e-8,0.0001586656,0.9632287,0.002104632,0.003904516,0.02275454,0.003510225],"study_design_scores_gemma":[0.000542763,0.00004934893,0.001241554,0.000001941022,0.0001528474,0.000002745112,0.00004682407,0.9824375,0.0004760257,0.0007073432,0.01408531,0.0002558042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03043234,0.0001288024,0.9666911,0.0000225071,0.0001121997,0.000299759,0.0003311163,0.00002185924,0.001960331],"genre_scores_gemma":[0.4069114,0.00004909853,0.5829618,0.0006928429,0.0006926758,0.00008904129,0.004161312,0.00004601882,0.004395836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3837293,"threshold_uncertainty_score":0.7430993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901459151223892,"score_gpt":0.2253018538962738,"score_spread":0.2062872623840349,"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."}}