{"id":"W4253132939","doi":"10.3410/f.11414978.12453055","title":"Faculty Opinions recommendation of Quantitative epistasis analysis and pathway inference from genetic interaction data.","year":2011,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Epistasis; Inference; Computational biology; Computer science; Data science; Information retrieval; Machine learning; Statistics; Artificial intelligence; Biology; Genetics; Mathematics; Gene","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":[0.00217451,0.002617864,0.001699134,0.003153846,0.0007559212,0.002847907,0.00373146,0.002558746,0.08788916],"category_scores_gemma":[0.01192691,0.0010139,0.001927444,0.004084078,0.000397824,0.001353313,0.001961667,0.00225206,0.073681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324964,"about_ca_system_score_gemma":0.003523084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01798901,"about_ca_topic_score_gemma":0.05225269,"domain_scores_codex":[0.9986047,0.000235846,0.0001310426,0.0004412995,0.000421786,0.0001652707],"domain_scores_gemma":[0.9962081,0.001147199,0.0002787924,0.001098287,0.0007222344,0.0005453069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009021355,0.00004502136,0.001480276,0.0005336272,0.00007477461,0.00002913995,0.00001197745,0.0004344262,0.0002156438,0.000459293,0.9931654,0.003460207],"study_design_scores_gemma":[0.0007801942,0.00003945409,0.008467821,0.0003106698,0.0001357294,0.0001388906,0.00004962272,0.0036296,0.001281239,0.00339145,0.9817199,0.00005540916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003027175,0.00006835831,0.0004062678,0.0001415877,0.0000412796,0.00002246607,0.9965759,0.001396429,0.001044919],"genre_scores_gemma":[0.0008543924,0.00006423358,0.001184748,0.00008984397,0.000009763475,0.00007317587,0.9964503,0.0001633721,0.001110205],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08788916,"threshold_uncertainty_score":0.2940186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05281047522705533,"score_gpt":0.3658555189527225,"score_spread":0.3130450437256672,"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."}}