{"id":"W2261245081","doi":"10.1093/bioinformatics/btv755","title":"simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Semantic similarity; Computer science; Similarity (geometry); Similarity measure; Cosine similarity; Correlation; Measure (data warehouse); Data mining; Artificial intelligence; Natural language processing; Information retrieval; Theoretical computer science; Mathematics; Pattern recognition (psychology); Image (mathematics)","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":[0.0005992992,0.0002063963,0.0004812491,0.0001949946,0.00006420245,0.00001516266,0.0002227994,0.0003258045,0.00001133343],"category_scores_gemma":[0.0001638589,0.0001894559,0.0003618332,0.0002988933,0.0001642715,0.00001209542,0.00006092693,0.00008059691,0.000001915964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002430746,"about_ca_system_score_gemma":0.0002819133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001890388,"about_ca_topic_score_gemma":0.0001495915,"domain_scores_codex":[0.9984478,0.00003194574,0.0008429365,0.0001516414,0.0002613074,0.0002643864],"domain_scores_gemma":[0.9982621,0.00006400994,0.000534795,0.0004718512,0.0005394865,0.0001277438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006834195,0.003330583,0.2718091,0.005740004,0.02358002,0.000006183344,0.003987197,0.4566105,0.1144791,0.01326563,0.05153842,0.04881904],"study_design_scores_gemma":[0.004748265,0.001338537,0.01185219,0.00005194057,0.002970624,0.00001300739,0.0006538444,0.8950941,0.07245398,0.001841675,0.008077648,0.0009042017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2275293,0.0005989589,0.7673666,0.0001511236,0.0003789771,0.0005997817,0.001120689,0.00002157579,0.002232999],"genre_scores_gemma":[0.9375455,0.00002863725,0.05956443,0.0003965758,0.00008855989,0.00002284367,0.002317514,0.00001468863,0.00002122512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7100163,"threshold_uncertainty_score":0.7725787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04728626949081537,"score_gpt":0.2517290138241195,"score_spread":0.2044427443333041,"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."}}