{"id":"W4380869524","doi":"10.1093/bioinformatics/btad381","title":"GRETTA: an R package for mapping <i>in silico</i> genetic interaction and essentiality networks","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Genome British Columbia; University of British Columbia","funders":"University of British Columbia; University of Alabama at Birmingham; Canada Research Chairs; Broad Institute; University of Alabama; Canadian Institutes of Health Research; Canada's Michael Smith Genome Sciences Centre","keywords":"DECIPHER; In silico; Computer science; MIT License; Documentation; Computational biology; Source code; Biology; Genetics; Gene; Software; Programming language","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":[0.0003809483,0.0001646322,0.0001655419,0.00009056943,0.000101191,0.0000851482,0.0001412238,0.0001955705,0.000002951565],"category_scores_gemma":[0.00002941225,0.0001630636,0.0000612218,0.0001705285,0.00005254908,0.00002563948,0.0001233219,0.0001000714,0.000008778875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001439256,"about_ca_system_score_gemma":0.00002713064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009164136,"about_ca_topic_score_gemma":0.000049076,"domain_scores_codex":[0.9989068,0.00002118732,0.0004774338,0.0001649553,0.00007940509,0.0003502018],"domain_scores_gemma":[0.9993758,0.00002487196,0.0001466977,0.0003126313,0.00004244384,0.00009757355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006914242,0.0004161176,0.04085842,0.002827508,0.0005261371,0.00001933032,0.008965468,0.05265799,0.04984031,0.002842049,0.08694295,0.7534123],"study_design_scores_gemma":[0.001303158,0.0002686385,0.01364853,0.00006041584,0.00002339552,0.00003002237,0.001939208,0.9507687,0.001505679,0.0005624713,0.02937591,0.0005138973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7860469,0.0002311393,0.2115032,0.0001261739,0.0005751843,0.0006869143,0.00004939439,0.00005862289,0.0007224681],"genre_scores_gemma":[0.9871578,0.0005232723,0.01046992,0.0004976389,0.0003748429,0.00005567559,0.0007566875,0.00002796888,0.0001361526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8981107,"threshold_uncertainty_score":0.6649539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750650609130439,"score_gpt":0.2602235399463445,"score_spread":0.2427170338550401,"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."}}