{"id":"W4313415333","doi":"10.3390/life13010076","title":"Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs","year":2022,"lang":"en","type":"article","venue":"Life","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"DNA methylation; Computational biology; Gene; Biology; Methylation; KEGG; Biological network; DNA; Bilayer; Gene regulatory network; Bioinformatics; Genetics; Gene expression; Transcriptome; Membrane","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":[0.0004566573,0.0004169481,0.0003760314,0.001895292,0.0003074064,0.0006039928,0.0002782508,0.0002656185,0.0008886547],"category_scores_gemma":[0.001789574,0.0001937408,0.0006790209,0.001226561,0.0002965305,0.0008434207,0.0007070657,0.0004123574,0.0001931465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004755144,"about_ca_system_score_gemma":0.0004414523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002587483,"about_ca_topic_score_gemma":0.003436108,"domain_scores_codex":[0.9998036,0.00004836799,0.000009806913,0.00007108387,0.00003932961,0.00002790712],"domain_scores_gemma":[0.999424,0.0003087486,0.0001051598,0.00005052762,0.00007565271,0.00003596447],"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":[0.0011235,0.0001913758,0.09956446,0.000649554,0.0004055587,0.0007500358,0.0005138206,0.5797711,0.1347623,0.02928628,0.00208157,0.1509004],"study_design_scores_gemma":[0.00001472276,0.00006429005,0.02289521,0.00002352174,0.00009088468,0.0001494533,0.0001205681,0.9363422,0.00710255,0.03109782,0.002075614,0.0000230816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6775492,0.0006429087,0.315035,0.0003039351,0.00002251616,0.00008268691,0.003333219,0.0004123195,0.002618174],"genre_scores_gemma":[0.9227194,0.0005187082,0.07276157,0.00003616992,0.00001309361,0.00007555617,0.003193144,0.00003921455,0.0006431913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002587483,"threshold_uncertainty_score":0.005144894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505289572187927,"score_gpt":0.2328419054905825,"score_spread":0.2177890097687032,"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."}}