{"id":"W2908850813","doi":"10.1016/j.csbj.2019.01.001","title":"Computational drug repurposing for inflammatory bowel disease using genetic information","year":2019,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Natural Sciences and Engineering Research Council of Canada; Children’s Hospital Foundation of Manitoba; Manitoba Health Research Council; Children's Hospital Foundation; Health Sciences Centre Foundation","keywords":"Drug repositioning; Genome-wide association study; Repurposing; Computational biology; Candidate gene; Drug; Disease; Inflammatory bowel disease; Gene; Identification (biology); Drug discovery; Genetic association; Pharmacogenomics; Biology; Bioinformatics; Medicine; Genetics; Single-nucleotide polymorphism; Pharmacology; Genotype","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":[0.0004457486,0.0007699475,0.001112179,0.0008960032,0.0005495153,0.0009202863,0.001038678,0.001145845,0.005088059],"category_scores_gemma":[0.001707312,0.0005329517,0.001447923,0.0007819344,0.0003926999,0.0005028134,0.0006963285,0.0008299063,0.0003939786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008846408,"about_ca_system_score_gemma":0.002096693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009275307,"about_ca_topic_score_gemma":0.01320189,"domain_scores_codex":[0.9998602,0.00004786294,0.000008261237,0.00002429759,0.00003442477,0.00002496317],"domain_scores_gemma":[0.999283,0.0005455751,0.00004030566,0.00003902421,0.00005197572,0.00004018491],"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":[0.00009490456,0.0000723602,0.00134064,0.0001038613,0.00006685386,0.0001906615,0.00002409332,0.9814118,0.0008345285,0.004978794,0.0009742103,0.009907393],"study_design_scores_gemma":[0.00002690701,0.0000217979,0.0001020742,0.000003622352,0.00002073567,0.00002084234,0.000009833597,0.9957455,0.0003122068,0.003063438,0.0006690096,0.000004057048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.476193,0.002007058,0.4771456,0.002921822,0.0002235273,0.000467897,0.004845051,0.005200926,0.03099526],"genre_scores_gemma":[0.7621394,0.0008029286,0.2263068,0.0005707781,0.00007091984,0.0007032022,0.003387418,0.0003301804,0.005688419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009275307,"threshold_uncertainty_score":0.01844263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004274952740551562,"score_gpt":0.221909202392394,"score_spread":0.2176342496518424,"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."}}