{"id":"W2107521571","doi":"10.3389/fgene.2012.00228","title":"Exploiting Gene Expression Variation to Capture Gene-Environment Interactions for Disease","year":2013,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Gene; Gene expression; Genetics; Computational biology; Biology; Variation (astronomy); Perspective (graphical); Regulation of gene expression; Computer science; Artificial intelligence","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":[0.001235905,0.0004593915,0.0007516945,0.001440778,0.0003349657,0.0006822739,0.0005259818,0.0005412679,0.001049687],"category_scores_gemma":[0.002185318,0.000233153,0.0007038547,0.002344692,0.0006006804,0.0004222886,0.0005360597,0.001010372,0.0002478978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003929712,"about_ca_system_score_gemma":0.0003396988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001758766,"about_ca_topic_score_gemma":0.002285625,"domain_scores_codex":[0.9993568,0.0002442895,0.00002513114,0.0002472719,0.0000745178,0.00005186463],"domain_scores_gemma":[0.9987463,0.0008851645,0.0001768368,0.0001102103,0.00003518516,0.00004623613],"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":[0.001007894,0.0003572906,0.3605385,0.0005223514,0.001627863,0.001405129,0.0005824873,0.06689431,0.3803471,0.02774461,0.001988922,0.1569834],"study_design_scores_gemma":[0.000114374,0.0005298875,0.5140426,0.00007902038,0.001004322,0.001887784,0.0003256752,0.2801823,0.05697271,0.1259258,0.01877247,0.0001630126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5741047,0.004260384,0.4110964,0.001133029,0.0001007418,0.00009268703,0.0046275,0.0008186258,0.003765973],"genre_scores_gemma":[0.9417849,0.00120394,0.05453908,0.000385983,0.00006199939,0.00008002005,0.001367824,0.00008748679,0.0004888285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001758766,"threshold_uncertainty_score":0.006536126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006531437272453384,"score_gpt":0.2091946769571051,"score_spread":0.2026632396846517,"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."}}