{"id":"W2107546215","doi":"10.1093/nar/gkt400","title":"SGAtools: one-stop analysis and visualization of array-based genetic interaction screens","year":2013,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":194,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research","keywords":"Biology; Genetic analysis; Computational biology; Visualization; Genome; Genetic screen; Genetics; Mutant; Gene; Artificial intelligence; Computer science","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":[0.0003893485,0.00009140052,0.0001512768,0.0002420729,0.00009366315,0.00006422107,0.000154667,0.0001490452,0.000177677],"category_scores_gemma":[0.00006884131,0.00008794288,0.00007263125,0.0004508013,0.0001555973,0.000009523092,0.00009017078,0.0001289821,0.0000251148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001675611,"about_ca_system_score_gemma":0.00004978644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001575148,"about_ca_topic_score_gemma":0.00004216417,"domain_scores_codex":[0.9989165,0.000106348,0.0002616674,0.0002155383,0.0002549692,0.0002449742],"domain_scores_gemma":[0.9991314,0.00002914154,0.00007966577,0.0003306922,0.0003317795,0.00009735896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001472507,0.0001985421,0.04574339,0.0001478325,0.0006535036,6.928027e-7,0.0003029811,0.001155526,0.8871559,0.0005142014,0.002988645,0.06099155],"study_design_scores_gemma":[0.002195413,0.001972844,0.3140315,0.0001143457,0.0003448249,0.000008574032,0.001561318,0.2095206,0.4507898,0.001469832,0.01716353,0.0008274383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9260605,0.0002252481,0.07135899,0.0001442496,0.00002584938,0.0002749285,0.00000845739,0.000007185824,0.001894587],"genre_scores_gemma":[0.9949303,0.0001310123,0.004455927,0.00008465585,0.00007644062,0.00002271451,0.0001122742,0.00001470629,0.0001719152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4363661,"threshold_uncertainty_score":0.3586207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03178198603480616,"score_gpt":0.3299208541634189,"score_spread":0.2981388681286128,"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."}}