{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00171107,0.0008738143,0.0009509827,0.008163026,0.0005936026,0.001398443,0.001052045,0.0006615694,0.004402825],"category_scores_gemma":[0.009453135,0.0002166812,0.001221016,0.006399423,0.0006810292,0.002160998,0.001731607,0.0008396503,0.001292011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009798729,"about_ca_system_score_gemma":0.001016015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001197852,"about_ca_topic_score_gemma":0.00158793,"domain_scores_codex":[0.9973795,0.0004423909,0.0004263371,0.0005557447,0.001063407,0.0001325279],"domain_scores_gemma":[0.9965705,0.001332522,0.0006456258,0.0004466484,0.0008456428,0.0001590511],"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":[0.002237723,0.0006023779,0.07621443,0.004675504,0.0009445543,0.0006100496,0.001373712,0.02441379,0.1147561,0.04921908,0.03098371,0.693969],"study_design_scores_gemma":[0.0004600851,0.002099767,0.1589114,0.0007005892,0.0005540828,0.004760185,0.002230112,0.4503315,0.1147079,0.1572637,0.1074611,0.0005194608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2445352,0.002445079,0.6969336,0.0003390642,0.0002073923,0.0007178667,0.03772667,0.008609592,0.008485672],"genre_scores_gemma":[0.457518,0.0005867225,0.491634,0.0001530443,0.00008733285,0.00104539,0.0468244,0.0007391933,0.001411881],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008163026,"threshold_uncertainty_score":0.0147289,"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."}}