{"id":"W4210368516","doi":"10.3390/jpm12020199","title":"Recent Major Transcriptomics and Epitranscriptomics Contributions toward Personalized and Precision Medicine","year":2022,"lang":"en","type":"review","venue":"Journal of Personalized Medicine","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Precision medicine; Personalized medicine; Transcriptome; Medicine; Computer science; Computational biology; Bioinformatics; Biology; Pathology; Genetics; Gene expression","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001599921,0.0004729702,0.001947757,0.0002958607,0.00020486,0.0000199729,0.0003312899,0.0003318677,0.0009555974],"category_scores_gemma":[0.0006076433,0.0003253504,0.0003577304,0.0002643637,0.0007825299,0.00001191571,0.00005694039,0.0006735428,5.718855e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001676444,"about_ca_system_score_gemma":0.0005480413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001485159,"about_ca_topic_score_gemma":0.00000341968,"domain_scores_codex":[0.9970507,0.0003765154,0.00125434,0.0004600332,0.0005614996,0.0002969248],"domain_scores_gemma":[0.9979076,0.0001539962,0.0009394613,0.0002869854,0.000343471,0.0003684397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005523063,0.00008842056,0.000003706667,0.002145373,0.0009034863,0.00003197128,0.00105559,3.04778e-7,0.003490301,0.000702456,0.01690708,0.974119],"study_design_scores_gemma":[0.004646078,0.00106444,0.000004985468,0.002718872,0.002578431,0.00141458,0.0007728685,0.000005080995,0.00001248432,0.00011119,0.9863832,0.0002877799],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002299685,0.9929362,0.001556982,0.003564622,0.0007529585,0.0005260856,0.0002041609,0.000006021936,0.0002230346],"genre_scores_gemma":[0.00005869687,0.996573,0.000328449,0.0004777335,0.001223524,0.00004746609,0.000352319,0.00005912945,0.0008796994],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9738312,"threshold_uncertainty_score":0.9999577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06072326530885462,"score_gpt":0.3607140481321376,"score_spread":0.299990782823283,"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."}}