{"id":"W3006386970","doi":"10.1109/tcbb.2020.2973148","title":"FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers","year":2020,"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":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Hunan Provincial Innovation Foundation for Postgraduate; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Breast cancer; Interactome; Discriminative model; Cancer; Computational biology; Disease; Genetic heterogeneity; Heterogeneous network; Oncology; Gene; Bioinformatics; Medicine; Biology; Internal medicine; Computer science; Machine learning; Genetics; Phenotype; Wireless network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002213174,0.0002450515,0.0002181037,0.00007322028,0.0002820769,0.00005605038,0.0001650653,0.0002373883,0.00003146975],"category_scores_gemma":[0.00001354195,0.0002179813,0.00008058183,0.000183689,0.0001170833,0.00001284875,0.00002965158,0.0001566089,0.00001274383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337832,"about_ca_system_score_gemma":0.00008539239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008426842,"about_ca_topic_score_gemma":0.00001598043,"domain_scores_codex":[0.9988274,0.00007273628,0.0004199899,0.0002801687,0.0001049142,0.0002948132],"domain_scores_gemma":[0.9992269,0.000126061,0.0001233176,0.000161975,0.0001015228,0.0002601887],"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":[0.001107274,0.00009765863,0.002381346,0.0002350822,0.0005460656,0.000002945604,0.0003071734,0.5610191,0.004060933,0.00007542336,0.00215303,0.428014],"study_design_scores_gemma":[0.002329377,0.001550938,0.007278943,0.0001268819,0.0002129798,0.0002128867,0.0001398486,0.975574,0.00240094,0.001383978,0.007820393,0.0009688751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05462027,0.0002293596,0.941127,0.00276015,0.0002370651,0.0005074836,0.000448811,0.0000324503,0.00003740697],"genre_scores_gemma":[0.842562,0.0001712637,0.1488414,0.007879926,0.0001893879,0.00007330603,0.0002517071,0.00002002038,0.00001098786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7922856,"threshold_uncertainty_score":0.8889021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493578943012598,"score_gpt":0.2929880657424414,"score_spread":0.2780522763123154,"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."}}