{"id":"W2950147502","doi":"10.1101/668335","title":"Humanization of yeast genes with multiple human orthologs reveals principles of functional divergence between paralogs","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Welch Foundation; University of Washington; University of Toronto; American Heart Association; National Institutes of Health; National Science Foundation","keywords":"Biology; Gene; Lineage (genetic); Gene duplication; Functional divergence; Genetics; In silico; Gene family; Function (biology); Yeast; Evolutionary biology; Computational biology; Genome","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":[0.0004057389,0.0001703129,0.0002971667,0.000481225,0.0001791976,0.0004313853,0.0002109973,0.0002964849,0.0007404139],"category_scores_gemma":[0.000812093,0.0001557811,0.000266504,0.0004467894,0.0003205179,0.0002208446,0.0003588297,0.000364806,0.0001881489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002144078,"about_ca_system_score_gemma":0.0001253168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005482885,"about_ca_topic_score_gemma":0.0008616245,"domain_scores_codex":[0.9996601,0.00005355215,0.00003100741,0.0001168748,0.00009750483,0.00004093311],"domain_scores_gemma":[0.9993138,0.0002839887,0.0001925346,0.00009769552,0.00006099682,0.00005097704],"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":[0.0002243759,0.00002343687,0.02471625,0.00007130409,0.00004089116,0.000184451,0.00009454945,0.0008716004,0.9697773,0.0002720427,0.00004390769,0.003679794],"study_design_scores_gemma":[0.00002698342,0.0004505703,0.298786,0.00002961238,0.0001028441,0.003004409,0.0004636031,0.01476055,0.6774698,0.0009774556,0.003888304,0.00003986902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980156,0.0002620078,0.001390834,0.0000151466,0.000003021798,0.000002246653,0.00009988076,0.00002246836,0.0001887665],"genre_scores_gemma":[0.9986454,0.000052682,0.0009892429,0.00001702167,0.000001111161,0.000002855598,0.0001787675,0.000009703799,0.0001033126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007404139,"threshold_uncertainty_score":0.002476931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939230754749008,"score_gpt":0.2149959247761672,"score_spread":0.1956036172286771,"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."}}