{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143386,0.0007284561,0.0008302539,0.001537031,0.0006751263,0.0007482622,0.001232635,0.0006328618,0.002929004],"category_scores_gemma":[0.002580141,0.0004923625,0.001024096,0.0009224996,0.0005137674,0.0006523364,0.001667151,0.0004103706,0.0009938319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005575072,"about_ca_system_score_gemma":0.0008802824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683949,"about_ca_topic_score_gemma":0.00427861,"domain_scores_codex":[0.9991617,0.0002042582,0.00004342857,0.0002977395,0.0002224008,0.00007045376],"domain_scores_gemma":[0.9993167,0.000294668,0.0001210658,0.0001130513,0.0001011118,0.00005344499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001746133,0.0003098765,0.03262659,0.001132149,0.0009407119,0.000659322,0.0008580576,0.2369612,0.1883475,0.01788069,0.01353532,0.5050026],"study_design_scores_gemma":[0.00009884766,0.0002129666,0.006943334,0.00002995794,0.0001152843,0.0004771901,0.0001137633,0.9327053,0.04351516,0.00903549,0.006689954,0.00006272297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.172209,0.0006651044,0.8127729,0.0001413046,0.00004266403,0.000250503,0.001135167,0.009841996,0.002941353],"genre_scores_gemma":[0.4242006,0.0001831532,0.5692021,0.000104295,0.00002487119,0.0003688023,0.002556547,0.0008056589,0.002553956],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002929004,"threshold_uncertainty_score":0.009798467,"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."}}