{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002813099,0.0001477315,0.0003280474,0.0001744147,0.000125116,0.0000106067,0.0001753811,0.00008956218,0.00001781033],"category_scores_gemma":[0.00003340989,0.000141086,0.0001536147,0.0002738764,0.0001100523,0.000009454734,0.000199126,0.00006420324,2.574957e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001367228,"about_ca_system_score_gemma":0.00004987766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006828488,"about_ca_topic_score_gemma":0.00001616423,"domain_scores_codex":[0.998853,0.00001845122,0.0006084506,0.0001492095,0.0001531854,0.0002176988],"domain_scores_gemma":[0.9990543,0.00002396849,0.0004287994,0.0003033018,0.0001371844,0.00005240676],"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":[0.001996874,0.0005905401,0.03274742,0.001639985,0.008770561,0.000002567188,0.006226358,0.1700249,0.6338335,0.00290788,0.005537327,0.1357221],"study_design_scores_gemma":[0.001861337,0.001324564,0.008824077,0.000004719301,0.00133206,0.00008894885,0.001123542,0.8750778,0.09411027,0.0001829001,0.01549806,0.0005717609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.925409,0.001422164,0.07161244,0.00009677777,0.000305007,0.0004620805,0.0004207209,0.000009109621,0.0002627534],"genre_scores_gemma":[0.9557949,0.00009255243,0.0422749,0.0002392284,0.00008909624,0.00005180874,0.001358558,0.00001452189,0.00008447097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7050529,"threshold_uncertainty_score":0.5753321,"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."}}