{"id":"W1992250714","doi":"10.1371/journal.pcbi.1002559","title":"Multiple Genetic Interaction Experiments Provide Complementary Information Useful for Gene Function Prediction","year":2012,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Phenotype; Computational biology; Function (biology); Gene interaction; Saccharomyces cerevisiae; Computer science; Genetic screen; Gene; Biology; Genetic network; Exploit; Genetic model; Genetic analysis; Gene regulatory network; Interaction network; 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":[0.000117527,0.0001395168,0.0001099588,0.00006922594,0.0001373976,0.0000222408,0.00008363428,0.0001288444,0.0000255686],"category_scores_gemma":[0.00002947841,0.0001388412,0.00006565472,0.00004368545,0.00003696291,0.00004540594,0.00006592024,0.00006026932,0.00003045463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004289267,"about_ca_system_score_gemma":0.00003332582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006928885,"about_ca_topic_score_gemma":0.000002548451,"domain_scores_codex":[0.9990966,0.00003995871,0.000382348,0.000144455,0.00007883881,0.0002577715],"domain_scores_gemma":[0.9994541,0.00004075773,0.000179801,0.0001194969,0.0001314839,0.00007437209],"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":[0.003677497,0.001460289,0.3299263,0.0003186814,0.00199078,2.535982e-7,0.002084579,0.07611487,0.460777,0.005599847,0.03207539,0.08597454],"study_design_scores_gemma":[0.007526259,0.003112536,0.1095649,0.00003877074,0.0002292978,0.00007528099,0.0007551634,0.434089,0.06001797,0.006495269,0.3769999,0.001095651],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4833785,0.0001732333,0.5146962,0.00006357324,0.0007398751,0.0005908104,0.0002000294,0.00002510617,0.000132656],"genre_scores_gemma":[0.9637836,0.00001136263,0.02345807,0.0007017733,0.0006366735,0.0001812183,0.01120176,0.00001192521,0.0000136016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4912382,"threshold_uncertainty_score":0.566178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473098982520433,"score_gpt":0.261625823655466,"score_spread":0.2368948338302617,"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."}}