{"id":"W2119722989","doi":"10.1186/1471-2105-7-391","title":"Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression","year":2006,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Replicate; False positive paradox; Computer science; DNA microarray; Linear model; Replication (statistics); Set (abstract data type); Mixture model; Data set; Expression (computer science); Pattern recognition (psychology); Data mining; Gaussian; Computational biology; Artificial intelligence; Mathematics; Statistics; Gene expression; Gene; Biology; Genetics; Machine learning","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":[0.0001205377,0.0001222985,0.0001209116,0.00006564523,0.00005449474,0.00001678094,0.0000914225,0.0001851341,7.025342e-7],"category_scores_gemma":[0.00008286844,0.0001060143,0.00003749923,0.00007900537,0.00002233235,0.00001658804,0.00008350652,0.00008467055,5.298789e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001200357,"about_ca_system_score_gemma":0.00004953165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002141065,"about_ca_topic_score_gemma":0.00003757919,"domain_scores_codex":[0.9990487,0.0000223716,0.0004938899,0.0001628161,0.0001314716,0.0001407892],"domain_scores_gemma":[0.999424,0.000009708546,0.0002287334,0.0002301662,0.000074223,0.00003313595],"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":[0.0001549143,0.00003389044,0.0001690781,0.00009491519,0.00000230082,5.130563e-8,0.0001002431,0.08800977,0.9094454,0.00001550338,0.00005243568,0.001921511],"study_design_scores_gemma":[0.0006490836,0.00002584576,0.0004619574,0.00003388364,0.000004558422,0.000001289504,0.00005925825,0.5975332,0.4010373,0.00009035966,0.00002213803,0.00008108931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4956721,0.00006954883,0.5039999,0.00001070146,0.00003634428,0.0001154391,0.00002586375,0.000006610072,0.00006350638],"genre_scores_gemma":[0.8941904,0.00008108306,0.1053402,0.00001401359,0.00008519398,0.00001445102,0.000190064,0.000009883049,0.00007461029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5095234,"threshold_uncertainty_score":0.4323139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578516164332486,"score_gpt":0.2511201571694699,"score_spread":0.2253349955261451,"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."}}