{"id":"W2143693563","doi":"10.1093/bioinformatics/btu373","title":"Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bioconductor; microRNA; Computational biology; Cluster analysis; Computer science; Biology; Gene regulatory network; Gene; Gene expression; Machine learning; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244706,0.0002189869,0.0001697634,0.00006238691,0.0001644025,0.00005917381,0.0002036915,0.0001978858,0.00002250403],"category_scores_gemma":[0.0002564283,0.0002178082,0.0001043548,0.00009287289,0.00006406028,0.00001867707,0.0001219291,0.00007953163,0.00003252946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004032665,"about_ca_system_score_gemma":0.00004219492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001016473,"about_ca_topic_score_gemma":0.000005609724,"domain_scores_codex":[0.9987725,0.00004805873,0.0004232541,0.0002152729,0.0002267961,0.0003141452],"domain_scores_gemma":[0.9989679,0.00002398401,0.0002479696,0.0005402348,0.00008177575,0.0001380894],"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":[0.00002666215,0.00003607233,0.0002307157,0.000140129,0.00004541151,3.674281e-7,0.00008110305,0.0006754969,0.9687737,0.0002897829,0.005286642,0.02441393],"study_design_scores_gemma":[0.001566681,0.0003114145,0.01044935,0.0002279084,0.00009613446,0.00003037386,0.0003256638,0.1584902,0.7749834,0.0003439017,0.05203494,0.00113997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167828,0.0003845323,0.08018164,0.00003223805,0.0002088401,0.0001621154,0.00002158754,0.00005924736,0.002166989],"genre_scores_gemma":[0.9923314,0.00002896458,0.006736075,0.0001991865,0.000215865,0.00001243121,0.0002516363,0.0000338443,0.000190548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1937903,"threshold_uncertainty_score":0.8881959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006147777979144508,"score_gpt":0.2108589855606011,"score_spread":0.2047112075814566,"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."}}