{"id":"W4392376129","doi":"10.1101/2024.03.01.583008","title":"Expression based polygenic scores - A gene network perspective to capture individual differences in biological processes","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Hope for Depression Research Foundation","keywords":"Biology; Genome-wide association study; Computational biology; Genetics; Gene; Gene expression; Genotyping; Genetic association; Single-nucleotide polymorphism; Expression quantitative trait loci; Phenotype; 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":[0.001973276,0.0008121541,0.0006735146,0.001883943,0.0002873673,0.001197434,0.0005245113,0.0004805773,0.001404699],"category_scores_gemma":[0.004946359,0.0002390745,0.0007215476,0.002185131,0.000844428,0.001402504,0.0008501578,0.00130446,0.000241882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005281523,"about_ca_system_score_gemma":0.0005434945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002135497,"about_ca_topic_score_gemma":0.00264947,"domain_scores_codex":[0.9991289,0.0004644889,0.00003743326,0.0001902668,0.0001257584,0.00005315692],"domain_scores_gemma":[0.9978315,0.00131054,0.000285901,0.0003284004,0.0001530372,0.0000904464],"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":[0.0002903222,0.0001874263,0.1147844,0.0003238274,0.001013225,0.0004074546,0.0004781432,0.3616298,0.02942651,0.2852978,0.003527974,0.2026331],"study_design_scores_gemma":[0.00003033658,0.0001416785,0.04862819,0.00004560745,0.0002007916,0.0003440988,0.00008868011,0.5771242,0.003406822,0.3635584,0.006377611,0.00005352143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0765243,0.0007668289,0.9181502,0.0007086035,0.00004509346,0.00006091162,0.001115834,0.0003055065,0.002322618],"genre_scores_gemma":[0.8287493,0.001054056,0.1653608,0.0003393218,0.0001480581,0.0001967621,0.00158378,0.0001245141,0.002443378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002135497,"threshold_uncertainty_score":0.01043576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545005028066176,"score_gpt":0.2247691282939071,"score_spread":0.2093190780132453,"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."}}