{"id":"W4298004368","doi":"10.1038/s41467-022-32887-9","title":"Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Institute of Genetics; University of Tokyo; Japan Agency for Medical Research and Development","keywords":"Transcriptome; Biology; In silico; Computational biology; Intron; Genome; Massive parallel sequencing; RNA splicing; Genetics; Identification (biology); Genomics; Gene; RNA; Gene expression","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":[0.0009294042,0.0001008645,0.0001756646,0.00007980168,0.0004022366,0.00004599137,0.002044821,0.0001609935,0.00009275897],"category_scores_gemma":[0.0004549813,0.0001096245,0.0000647207,0.0003552953,0.00006301307,0.00002880972,0.0005994177,0.0004024408,0.00000788393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389217,"about_ca_system_score_gemma":0.0001957787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001902556,"about_ca_topic_score_gemma":0.0002765622,"domain_scores_codex":[0.9982565,0.0005088861,0.0005081987,0.0003516757,0.0002481633,0.0001265901],"domain_scores_gemma":[0.9952314,0.00005311843,0.0005087469,0.003904571,0.0002683078,0.00003378584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001592851,0.0001655943,0.0005375462,0.0001118127,0.0002523104,1.885339e-7,0.0001566151,0.0001106697,0.9876143,0.001577041,0.009376685,0.00008131967],"study_design_scores_gemma":[0.0132178,0.001612884,0.112385,0.003484812,0.006647549,0.00008950761,0.03370463,0.306874,0.2796119,0.006745766,0.229645,0.005981151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8746655,0.08243784,0.01154944,0.006785898,0.001796398,0.003624622,0.01410828,0.0001755304,0.004856483],"genre_scores_gemma":[0.9836863,0.0005301517,0.0007653371,0.0001025159,0.00002895894,0.0002293717,0.01390996,0.00001846107,0.0007289237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7080024,"threshold_uncertainty_score":0.4470358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04471969934229081,"score_gpt":0.2896538756687049,"score_spread":0.2449341763264141,"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."}}