{"id":"W3165409583","doi":"10.1186/s12859-021-04215-3","title":"Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"BC Cancer Agency; National Natural Science Foundation of China; Cancer Research UK","keywords":"Subtyping; Breast cancer; microRNA; Oncology; Cancer; Computational biology; Bioinformatics; Biology; Medicine; Internal medicine; Gene; Computer science; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001825782,0.0001057497,0.0001290189,0.00004495857,0.00004674029,0.00004300421,0.0001254355,0.0001052526,0.00001356615],"category_scores_gemma":[0.0000500217,0.0000852758,0.00004966135,0.0001662492,0.00006653558,0.000009707636,0.0002078397,0.0001128136,0.000001454544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002273889,"about_ca_system_score_gemma":0.0002321007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004610069,"about_ca_topic_score_gemma":0.0008042112,"domain_scores_codex":[0.9992269,0.00003756809,0.0002641267,0.0001275664,0.0001422515,0.0002015768],"domain_scores_gemma":[0.999509,0.0000137974,0.000102219,0.0002249703,0.0001100151,0.00004002071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001107625,0.00004913762,0.01088212,0.0003926772,0.00007676238,0.000004114308,0.0006483468,0.0008726404,0.9655915,0.00005142343,0.0001027977,0.02121776],"study_design_scores_gemma":[0.0006287653,0.00005113798,0.02172898,0.0001996411,0.00002382704,0.0001134484,0.0009879908,0.005404936,0.9679952,0.00003108659,0.002642026,0.0001929767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99096,0.002476806,0.005755246,0.000247892,0.00007982604,0.0002129216,0.00007910083,0.000005828554,0.0001823761],"genre_scores_gemma":[0.9916143,0.002497607,0.005411037,0.0001976684,0.00007640494,0.00003291866,0.00005355235,0.00002294015,0.00009357358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02102478,"threshold_uncertainty_score":0.3477446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020827253097945,"score_gpt":0.2552147196282357,"score_spread":0.2450064470972563,"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."}}