{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009810238,0.0009235598,0.0004913224,0.002647534,0.0002509467,0.0005366133,0.000348764,0.0003516432,0.0005603299],"category_scores_gemma":[0.00264918,0.0002199488,0.000522178,0.001304202,0.0001891962,0.0007472119,0.0006642748,0.0004487757,0.0002922124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003607934,"about_ca_system_score_gemma":0.0004390091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001716828,"about_ca_topic_score_gemma":0.003957769,"domain_scores_codex":[0.9995742,0.0001224456,0.0000334294,0.0001501119,0.00008992723,0.00002974887],"domain_scores_gemma":[0.999166,0.0004684785,0.00015561,0.00006338716,0.00009870761,0.00004790064],"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":[0.0007139772,0.0003708852,0.2753059,0.000452555,0.0007228199,0.001043204,0.0001723991,0.160924,0.06965886,0.002677067,0.004981085,0.4829773],"study_design_scores_gemma":[0.00003326624,0.0002070331,0.04662568,0.00003745071,0.0002847987,0.0007371356,0.0001215782,0.9136722,0.0206524,0.01235135,0.005236969,0.00004011771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3454789,0.001782953,0.6419951,0.001057188,0.0000694507,0.0001903597,0.004630145,0.002147131,0.002648742],"genre_scores_gemma":[0.8415278,0.0008378851,0.1511938,0.0003080832,0.00009065381,0.00009882011,0.005145051,0.00007251951,0.0007254086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002647534,"threshold_uncertainty_score":0.005188167,"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."}}