{"id":"W4379520413","doi":"10.1101/2023.06.05.543335","title":"Illuminating Dark Proteins using Reactome Pathways","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Common Fund; National Institute on Drug Abuse; National Human Genome Research Institute; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; NIH Office of the Director; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health","keywords":"Computer science; Computational biology; Visualization; Random forest; Fuzzy logic; Biological pathway; Gene; Bioinformatics; Artificial intelligence; Biology; Gene expression; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001972232,0.001636386,0.0007583011,0.005269221,0.0008080855,0.002057131,0.000945984,0.0007838611,0.003528651],"category_scores_gemma":[0.003469774,0.0005685369,0.001698122,0.003909743,0.000562847,0.001818509,0.00204186,0.001288175,0.001144968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008318773,"about_ca_system_score_gemma":0.001377859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00254202,"about_ca_topic_score_gemma":0.003913502,"domain_scores_codex":[0.9991035,0.0002448591,0.0000549926,0.0003030396,0.0002260147,0.00006752655],"domain_scores_gemma":[0.9986957,0.0006052853,0.0002501714,0.000198973,0.0001575481,0.00009246029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002364451,0.000406261,0.05871136,0.006842964,0.001567488,0.002443853,0.002019541,0.2547364,0.3584696,0.1012649,0.02672969,0.1844436],"study_design_scores_gemma":[0.0001426286,0.0001610217,0.01293646,0.0003625493,0.0003421077,0.0005940177,0.0006065395,0.5873855,0.1476676,0.1450372,0.1045599,0.0002045481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966052,0.004250033,0.6994409,0.00159885,0.000293996,0.0003460286,0.05760056,0.03102846,0.008835973],"genre_scores_gemma":[0.3470556,0.004219746,0.5924414,0.0005538497,0.00009177082,0.0005571084,0.05076438,0.002256753,0.002059272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005269221,"threshold_uncertainty_score":0.01180452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452337078943758,"score_gpt":0.2315212256543912,"score_spread":0.1969978548649536,"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."}}