{"id":"W2211791842","doi":"10.1101/010843","title":"Sharing and specificity of co-expression networks across 35 human tissues","year":2014,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Common Fund; National Institute of Neurological Disorders and Stroke; National Cancer Institute; NIH Office of the Director; National Human Genome Research Institute; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institutes of Health; Université de Genève; Broad Institute; University of Chicago; Harvard University; University of Pennsylvania; National Institute on Drug Abuse; University of Miami","keywords":"Computational biology; Biology; Gene regulatory network; Gene; Inference; Function (biology); Gene expression; Regulation of gene expression; Hierarchy; Computer science; Genetics; Artificial intelligence","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":[0.0007521635,0.0003582855,0.0004316157,0.00116634,0.0003721442,0.000630738,0.0003590935,0.0003966487,0.001666982],"category_scores_gemma":[0.002310026,0.0002644359,0.000721482,0.001219598,0.0004344044,0.0003256937,0.0006645461,0.0003943126,0.0005054012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005395951,"about_ca_system_score_gemma":0.0004344982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004332263,"about_ca_topic_score_gemma":0.007502552,"domain_scores_codex":[0.9989979,0.0002021415,0.00003967869,0.0005560689,0.0001164099,0.00008795934],"domain_scores_gemma":[0.9992155,0.0003812349,0.0001330827,0.0001470864,0.00007346176,0.0000496762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002429718,0.0002174642,0.5545892,0.001069568,0.002009777,0.00222787,0.001400266,0.09077236,0.1831985,0.009929793,0.01863005,0.1335255],"study_design_scores_gemma":[0.0001516364,0.0001875163,0.7393038,0.000107183,0.0007353595,0.003197172,0.0004738703,0.1673269,0.04316534,0.02437613,0.02087911,0.00009592313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9413958,0.00145086,0.0335441,0.0003234022,0.00002136691,0.00004357141,0.0197118,0.0005821529,0.002926848],"genre_scores_gemma":[0.9590707,0.000468321,0.0135631,0.0001417633,0.00002271605,0.00006717751,0.02532139,0.00009531093,0.001249546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004332263,"threshold_uncertainty_score":0.008614123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123573461410627,"score_gpt":0.2492045797549466,"score_spread":0.2368472336138839,"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."}}