{"id":"W2767734009","doi":"10.1109/tcbb.2017.2770120","title":"Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data","year":2017,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"OMIM : Online Mendelian Inheritance in Man; Disease; Gene; Computational biology; Logistic regression; Gene regulatory network; Computer science; Machine learning; Biology; Genetics; Gene expression; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006734401,0.0002296298,0.0002275692,0.00004327775,0.000821671,0.0001718347,0.0009637966,0.0002968794,0.00000758634],"category_scores_gemma":[0.0001264011,0.0001978862,0.00004634271,0.00003408592,0.0004888687,0.00008293436,0.0002313659,0.0003109659,0.000005722818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008000596,"about_ca_system_score_gemma":0.0001011884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001302831,"about_ca_topic_score_gemma":0.00002524695,"domain_scores_codex":[0.9985175,0.00006057491,0.0006511886,0.000426603,0.00009966684,0.0002445365],"domain_scores_gemma":[0.997672,0.0001357202,0.0003299636,0.001575617,0.00007540334,0.0002113045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008602161,0.0004447229,0.01070802,0.0001683226,0.001016892,0.000004132768,0.0001611947,0.01673745,0.0006285548,0.0003686846,0.03475233,0.9341495],"study_design_scores_gemma":[0.001067683,0.0002796767,0.0026048,0.00003629692,0.0001043634,0.00002856833,0.00006699418,0.9849555,0.00006008276,0.001139064,0.009367521,0.0002893746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03242282,0.0005989965,0.9602131,0.0007743341,0.0007974362,0.0002713433,0.004763905,0.00002342591,0.0001346305],"genre_scores_gemma":[0.9160647,0.003335051,0.06522874,0.001087263,0.0004797261,0.0000114409,0.01366349,0.00002016162,0.0001094767],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9682181,"threshold_uncertainty_score":0.8069565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0450372869415296,"score_gpt":0.3326698544504935,"score_spread":0.2876325675089639,"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."}}