{"id":"W4280528542","doi":"10.1093/bioinformatics/btac304","title":"deepSimDEF: deep neural embeddings of gene products and gene ontology terms for functional analysis of genes","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; University of Ottawa; Dalhousie University; Montreal Heart Institute","funders":"Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Gene ontology; Gene; Computational biology; Computer science; Genetics; Biology; Artificial intelligence; 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":[0.0005065341,0.001122485,0.0004676732,0.0008159022,0.0002553689,0.0006760194,0.001589961,0.0007896685,0.003729071],"category_scores_gemma":[0.001670702,0.0003007895,0.0006856857,0.000801995,0.0004997339,0.001171695,0.001004932,0.00157646,0.00131171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009186696,"about_ca_system_score_gemma":0.0008521086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005350661,"about_ca_topic_score_gemma":0.008200385,"domain_scores_codex":[0.9997492,0.0000360571,0.00001323166,0.000102301,0.00007079007,0.00002837375],"domain_scores_gemma":[0.9996102,0.0001353471,0.00004664757,0.00008389127,0.00008795402,0.00003602143],"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":[0.0007791524,0.0004891243,0.01420049,0.0008922277,0.0003136973,0.0003001929,0.0001659881,0.3431015,0.05390498,0.0253945,0.06470017,0.4957581],"study_design_scores_gemma":[0.00003451175,0.0000762968,0.001048609,0.0000276624,0.00001919065,0.00007676339,0.00001854352,0.9662497,0.01105734,0.01643299,0.004941796,0.00001656982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1500907,0.002491634,0.7892306,0.001419759,0.0002913995,0.0001585519,0.02168735,0.02870158,0.005928423],"genre_scores_gemma":[0.6488056,0.001011539,0.2941607,0.0007975091,0.00009163168,0.0002804156,0.04722632,0.000907274,0.006718885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005350661,"threshold_uncertainty_score":0.01247501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238335273723722,"score_gpt":0.2263508285891908,"score_spread":0.2139674758519536,"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."}}