{"id":"W2136265964","doi":"10.1093/bioinformatics/btt613","title":"Mouse model phenotypes provide information about human drug targets","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Drug repositioning; Phenotype; Computational biology; Drug; Identification (biology); Similarity (geometry); Drug development; Drug discovery; Biology; Phenotypic screening; Repurposing; Computer science; Bioinformatics; Genetics; Pharmacology; Artificial intelligence; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001285045,0.0001579307,0.0001327618,0.00005535848,0.0001307253,0.00009294614,0.0002440531,0.0001675146,0.00003027851],"category_scores_gemma":[0.0001170907,0.000127021,0.00006263519,0.00005313363,0.0001188696,0.00004473508,0.0001283408,0.00009768109,0.0002806815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001403357,"about_ca_system_score_gemma":0.00006006696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000384237,"about_ca_topic_score_gemma":0.000009690529,"domain_scores_codex":[0.9990495,0.00001234929,0.0003964109,0.00009045317,0.0001736185,0.0002776875],"domain_scores_gemma":[0.9993178,0.000007015077,0.0001525888,0.0002998224,0.0001144766,0.000108329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005953319,0.0002130422,0.001317111,0.0007235486,0.0001993621,5.589257e-7,0.006703423,0.001845069,0.09248764,0.002009405,0.5479224,0.3465189],"study_design_scores_gemma":[0.002702414,0.000640244,0.002056747,0.00008955082,0.00006306006,0.0000148603,0.003285723,0.275576,0.2049337,0.002152063,0.5068073,0.001678364],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066766,0.0003607554,0.07160933,0.0006354562,0.0002328506,0.0008256758,0.00008411652,0.0002383592,0.01933685],"genre_scores_gemma":[0.8807287,0.0001087793,0.1106962,0.002349308,0.0001737662,0.0001184482,0.0007876281,0.00002420491,0.005012949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3448406,"threshold_uncertainty_score":0.5179766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089253766113648,"score_gpt":0.2404724012477871,"score_spread":0.2295798635866506,"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."}}