{"id":"W3017871916","doi":"10.1093/bioinformatics/btaa246","title":"SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Weston Brain Institute; National Institute on Aging; Alzheimer's Association","keywords":"Genome-wide association study; Computer science; Inference; Pipeline (software); Scalability; SPARK (programming language); Computational biology; Expression quantitative trait loci; Genomics; Data mining; Biology; Gene; Genome; Artificial intelligence; Genetics; Database; Operating system; Single-nucleotide polymorphism; Programming language; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004513388,0.002420382,0.00264002,0.00236685,0.001425114,0.003328351,0.004837254,0.001342061,0.01658985],"category_scores_gemma":[0.01181269,0.001548862,0.004333915,0.002235255,0.001056478,0.002366605,0.003854604,0.002742223,0.008435351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188633,"about_ca_system_score_gemma":0.005361541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007713218,"about_ca_topic_score_gemma":0.009724269,"domain_scores_codex":[0.9980811,0.0002706439,0.0001617989,0.0007472964,0.0005424999,0.0001966578],"domain_scores_gemma":[0.9960815,0.002014139,0.0003123705,0.0006243464,0.0005229556,0.0004446455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004309501,0.0003593553,0.01860862,0.004576109,0.002546362,0.002086207,0.001014853,0.07915838,0.04064724,0.03400334,0.6221972,0.1904929],"study_design_scores_gemma":[0.002645037,0.000231873,0.01009306,0.000255084,0.0005502701,0.001088725,0.0002722124,0.6068595,0.02807847,0.1603486,0.1891148,0.0004623277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01296833,0.001619106,0.6268368,0.00121582,0.0004581417,0.0005719594,0.08563519,0.2653049,0.005389741],"genre_scores_gemma":[0.1293197,0.001471301,0.5910842,0.00137257,0.0003344867,0.001907614,0.2343159,0.03535846,0.004835808],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01658985,"threshold_uncertainty_score":0.0554986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794040463663865,"score_gpt":0.253130555931134,"score_spread":0.2351901512944953,"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."}}