{"id":"W2889879328","doi":"10.1371/journal.pbio.2006092","title":"Refined RIP-seq protocol for epitranscriptome analysis with low input materials","year":2018,"lang":"en","type":"article","venue":"PLoS Biology","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":220,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Hospital for Sick Children; Princess Margaret Cancer Centre; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Biology; RNA; N6-Methyladenosine; Transcriptome; Computational biology; RNA-Seq; Immunoprecipitation; Messenger RNA; Molecular biology; Genetics; Gene; Gene expression; Methyltransferase","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001152929,0.000140771,0.0002236727,0.0000744862,0.00009005178,0.00001871559,0.0001892294,0.0001673462,0.0002136221],"category_scores_gemma":[0.00002616494,0.0001017474,0.00007668429,0.0001772835,0.0001904018,0.000002750151,0.00002610267,0.00002888914,0.00001546554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001105867,"about_ca_system_score_gemma":0.00006107848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002151194,"about_ca_topic_score_gemma":0.00007403608,"domain_scores_codex":[0.9990377,0.00005535277,0.0002109059,0.0004080918,0.00004225626,0.0002456977],"domain_scores_gemma":[0.9992123,0.000007219645,0.0001023046,0.0004239506,0.0002044357,0.0000498088],"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":[0.0005927085,0.00009030301,0.001518374,0.00002837403,0.0004696162,1.946754e-7,0.00001529435,0.00000203658,0.9957579,0.000306896,0.0008124358,0.000405876],"study_design_scores_gemma":[0.001041737,0.001161446,0.002270452,0.00000811155,0.000144395,0.000001897953,0.000006993434,0.0000305966,0.8872329,0.000141536,0.1077722,0.0001877886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9317921,0.00002659554,0.0232798,0.0006962942,0.0001092941,0.042454,0.0003839182,0.00004634506,0.001211624],"genre_scores_gemma":[0.8447858,0.00000475543,0.002585453,0.0005636907,0.0005070856,0.1502601,0.0004339278,0.00002357547,0.0008355681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.108525,"threshold_uncertainty_score":0.414914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218196219910607,"score_gpt":0.3146054533632934,"score_spread":0.2927858313722327,"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."}}