{"id":"W2030364523","doi":"10.1371/journal.pcbi.1004105","title":"Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3’ Splice Site Selection in SF3B1-mutated Cancers","year":2015,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":218,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; University of California, San Diego; Canada's Michael Smith Genome Sciences Centre; California Institute for Regenerative Medicine","keywords":"RNA splicing; Biology; Genetics; Context (archaeology); Gene; Point mutation; Transcriptome; Mutation; splice; Computational biology; SSS*; Exon; Splice site mutation; Phenotype; Cancer; Medicine; Gene expression; RNA; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006573219,0.0002675932,0.0004883761,0.0002934945,0.0003827831,0.0005992833,0.0002717968,0.0003004677,0.001052592],"category_scores_gemma":[0.001242878,0.0002123817,0.0006207012,0.0006729503,0.0002496975,0.0002766246,0.000282238,0.0004050962,0.0002323598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004890863,"about_ca_system_score_gemma":0.0005539931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002010311,"about_ca_topic_score_gemma":0.00423666,"domain_scores_codex":[0.9997585,0.00005767211,0.00001599389,0.00009120308,0.00004137113,0.00003527171],"domain_scores_gemma":[0.9996163,0.0002069675,0.00005539865,0.00005323796,0.00003644466,0.00003158224],"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":[0.001394295,0.0001507689,0.1954863,0.0007315968,0.0004396169,0.0008299277,0.0006161344,0.09267393,0.6343407,0.007736367,0.002857766,0.06274258],"study_design_scores_gemma":[0.00008418623,0.0002219364,0.2117487,0.00004926874,0.0003178371,0.0006509675,0.0005612585,0.6614299,0.09981216,0.01655253,0.008506058,0.00006512286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687864,0.0003191574,0.02646922,0.0001912138,0.0000215027,0.00001257692,0.002635395,0.0005082194,0.001056374],"genre_scores_gemma":[0.9753195,0.0001752473,0.01911183,0.0001061311,0.00000763578,0.00002676384,0.004773854,0.0001089878,0.000369951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002010311,"threshold_uncertainty_score":0.003997147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769706672546491,"score_gpt":0.2688856498713015,"score_spread":0.2411885831458366,"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."}}