{"id":"W4366721767","doi":"10.1093/bioinformatics/btad276","title":"spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; European Commission","keywords":"microRNA; Computational biology; Computer science; Competing endogenous RNA; Data science; Biology; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001329065,0.0001708213,0.0001145689,0.000082529,0.0001648626,0.00005363397,0.0002449437,0.0001390059,0.0000209877],"category_scores_gemma":[0.00004668739,0.0001196554,0.0001007478,0.0002283855,0.00009035673,0.0000141863,0.000183701,0.00007105873,0.0002916541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002235428,"about_ca_system_score_gemma":0.00003518404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002926482,"about_ca_topic_score_gemma":0.00000704685,"domain_scores_codex":[0.9990243,0.00004680463,0.0003060687,0.0001569924,0.0002411042,0.0002247509],"domain_scores_gemma":[0.9990891,0.00002359444,0.000133327,0.0005970384,0.00007545405,0.00008153341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003413529,0.0001546597,0.002465444,0.0005115811,0.0003484162,0.0000184306,0.009265849,0.002389571,0.530135,0.00186211,0.3156256,0.1368819],"study_design_scores_gemma":[0.001080109,0.0002960934,0.07262646,0.00009268984,0.00004675174,0.00001703421,0.001476128,0.02122934,0.200134,0.0006222957,0.7016757,0.0007033931],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961507,0.001484115,0.0003661932,0.00009830018,0.0002889879,0.0003195505,0.00001137272,0.00006411137,0.001216703],"genre_scores_gemma":[0.9978346,0.0004344944,0.000640267,0.0001825058,0.0002072929,0.00002873218,0.0004237576,0.00002631697,0.0002220258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3860501,"threshold_uncertainty_score":0.4879408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007984792714195431,"score_gpt":0.2119267939623223,"score_spread":0.2039420012481269,"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."}}