{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001197818,0.0001740236,0.0001106751,0.0001596192,0.0003296968,0.00009507808,0.0001538349,0.0001477247,0.00001645038],"category_scores_gemma":[0.00006350459,0.0001659175,0.00009340465,0.00004734219,0.0001543687,0.00004848265,0.00009482932,0.0001659529,0.000006391529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839599,"about_ca_system_score_gemma":0.0002465078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.410652e-7,"about_ca_topic_score_gemma":3.847479e-7,"domain_scores_codex":[0.9989752,0.00003973379,0.0003725157,0.0002150379,0.0001720452,0.0002254356],"domain_scores_gemma":[0.9992314,0.00002261086,0.0002350361,0.0001207354,0.0002457648,0.0001444797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008514491,0.00001420918,0.1020425,0.0001703056,0.0001588133,0.00003519497,0.0001010239,0.8780637,0.004779103,0.004958872,0.0005952438,0.008229553],"study_design_scores_gemma":[0.00187705,0.00008259752,0.5339168,0.00004210966,0.00004424906,0.0006424781,0.00008969211,0.4289283,0.0003563996,0.03220648,0.001432975,0.0003808531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643391,0.0003487376,0.03420258,0.0003835007,0.0003016816,0.0003331711,0.00005730818,0.00002514214,0.000008714882],"genre_scores_gemma":[0.9708483,0.00001855776,0.02836628,0.0003646034,0.0001571577,0.000006143522,0.000204579,0.00001348714,0.00002086381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4491354,"threshold_uncertainty_score":0.6765921,"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."}}