{"id":"W4213076590","doi":"10.1093/bib/bbac072","title":"HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Modularity (biology); Probabilistic logic; Data mining; Machine learning; Systems biology; Gene regulatory network; Artificial intelligence; Computational biology; Gene; Biology; Genetics","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":[0.001413421,0.001140476,0.001207597,0.001852239,0.0004305526,0.0007010297,0.001724365,0.001223374,0.001949681],"category_scores_gemma":[0.003594009,0.0005251166,0.001677124,0.001296977,0.0004378715,0.00110309,0.001124511,0.001152115,0.0006760409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005395164,"about_ca_system_score_gemma":0.00115715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005365731,"about_ca_topic_score_gemma":0.005520619,"domain_scores_codex":[0.9994097,0.0001778591,0.00003353693,0.0001704677,0.0001468261,0.00006175908],"domain_scores_gemma":[0.9991627,0.0004227663,0.00009895091,0.00009546849,0.0001477352,0.00007247595],"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":[0.0004752539,0.0002210576,0.01922479,0.0003861676,0.0005991962,0.0003877304,0.0001964191,0.502969,0.01878555,0.009974801,0.01250133,0.4342787],"study_design_scores_gemma":[0.00001621286,0.00002594798,0.0006004298,0.000008669671,0.00002541998,0.00004940421,0.000006903665,0.9931846,0.001182775,0.004140134,0.0007486193,0.00001083304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01960753,0.0004999043,0.977128,0.0002619967,0.00004804115,0.00005562311,0.0003676209,0.00163894,0.0003922709],"genre_scores_gemma":[0.3969612,0.0006727507,0.5958759,0.0005401566,0.0002181505,0.0004178098,0.002208467,0.0004236801,0.002681986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005365731,"threshold_uncertainty_score":0.01066899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005672426830212509,"score_gpt":0.2295912032176622,"score_spread":0.2239187763874496,"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."}}