{"id":"W2017814498","doi":"10.1186/1752-0509-8-s3-s3","title":"Prediction of disease genes using tissue-specified gene-gene network","year":2014,"lang":"en","type":"article","venue":"BMC Systems Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Gene regulatory network; Gene; Disease; Computational biology; Context (archaeology); Gene prediction; Biology; Biological network; Phenotype; Genetics; Bioinformatics; Gene expression; Pathology; Medicine; Genome","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":[0.000449005,0.000590986,0.0003368928,0.001795081,0.0002108014,0.000394148,0.0003467276,0.0003536381,0.001034372],"category_scores_gemma":[0.001380587,0.0001194927,0.0007242193,0.0009492876,0.0001842885,0.000477747,0.0003515093,0.0003356607,0.0002390877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003881638,"about_ca_system_score_gemma":0.0004569477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002128195,"about_ca_topic_score_gemma":0.003498909,"domain_scores_codex":[0.9997003,0.00007192382,0.00001945061,0.0001239128,0.00005794202,0.00002657535],"domain_scores_gemma":[0.9993377,0.0003640486,0.0001297033,0.00004479504,0.00009116748,0.00003248721],"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":[0.00080884,0.0002389541,0.2634597,0.0006022994,0.0007000107,0.0012805,0.0002257802,0.440373,0.07134902,0.005279297,0.002282584,0.2134],"study_design_scores_gemma":[0.00001640614,0.0001213013,0.0449864,0.0000314165,0.0002158134,0.0006220713,0.00007030606,0.933736,0.01157196,0.006686487,0.001915873,0.00002607471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.374101,0.001087273,0.6189392,0.0002576749,0.00003514531,0.0001319145,0.002942897,0.0007029261,0.001802034],"genre_scores_gemma":[0.8871815,0.0005618432,0.1079332,0.00004751884,0.00002418563,0.0001098011,0.003140669,0.00003032841,0.0009709279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002128195,"threshold_uncertainty_score":0.004231572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03017726675490346,"score_gpt":0.2423365511669398,"score_spread":0.2121592844120364,"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."}}