{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002386534,0.001453633,0.001858321,0.005232176,0.0005483998,0.001678947,0.0008274611,0.0008658788,0.001594628],"category_scores_gemma":[0.009321168,0.0007128511,0.001279297,0.003726326,0.0007179959,0.002484374,0.001598781,0.00144557,0.0004585574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005059012,"about_ca_system_score_gemma":0.0003967433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006746697,"about_ca_topic_score_gemma":0.001514976,"domain_scores_codex":[0.9982771,0.0006271938,0.0001373465,0.000506301,0.0003935645,0.00005859038],"domain_scores_gemma":[0.9855067,0.01097434,0.0009417235,0.001778337,0.0005486783,0.0002503447],"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":[0.001897359,0.0007711929,0.08255336,0.002220395,0.003261588,0.001301194,0.0004427463,0.2670771,0.3496875,0.01867583,0.001941795,0.2701699],"study_design_scores_gemma":[0.00008448843,0.0003922061,0.05807015,0.0001371054,0.00112505,0.0007329768,0.0001593018,0.7463449,0.09017928,0.09452321,0.008009556,0.0002417643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2498074,0.001738887,0.7348015,0.0004585598,0.00007475945,0.000141897,0.008221356,0.002087179,0.002668527],"genre_scores_gemma":[0.6884027,0.00135773,0.3025477,0.0001758949,0.00007284465,0.0002508843,0.006476803,0.0002708844,0.0004446524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005232176,"threshold_uncertainty_score":0.0126214,"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."}}