{"id":"W4322624753","doi":"10.1172/jci159940","title":"CRISPR/Cas9 screen uncovers functional translation of cryptic lncRNA-encoded open reading frames in human cancer","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical Investigation","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"Congressionally Directed Medical Research Programs; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; U.S. Department of Defense; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Cancer Prevention and Research Institute of Texas; Bristol-Myers Squibb","keywords":"Biology; GATA3; Ribosome profiling; Transcription factor; CRISPR; Computational biology; Genetics; Gene; Cancer research; Translation (biology); Messenger RNA","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":[0.002399288,0.0001076458,0.0003235966,0.0002149451,0.00005385147,0.0000371073,0.000289627,0.0002817184,0.00006554807],"category_scores_gemma":[0.001009727,0.0001037472,0.0001769958,0.0004346395,0.0001432702,0.00003706471,0.00008603165,0.0004216525,0.000003933164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004956675,"about_ca_system_score_gemma":0.0005913003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002371237,"about_ca_topic_score_gemma":0.0001575762,"domain_scores_codex":[0.9977015,0.0003171838,0.001141783,0.0002255065,0.00043678,0.0001772218],"domain_scores_gemma":[0.9985769,0.0001325416,0.0006311774,0.0001752192,0.0003404145,0.0001437699],"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":[0.0005289725,0.00006592865,0.009409544,0.00003911861,0.0001524009,0.00001203916,0.00008967726,0.01831796,0.9605322,0.0003938782,0.004176042,0.006282217],"study_design_scores_gemma":[0.006651219,0.001948534,0.06537142,0.0007219002,0.0001433067,0.00001683538,0.0002848655,0.006681681,0.8968684,0.01544844,0.005474455,0.0003889668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990628,0.0002134508,0.006821084,0.001322981,0.0003972303,0.0002687674,0.00001308973,0.000005476576,0.0003299174],"genre_scores_gemma":[0.996817,0.0005206417,0.001654064,0.0002685665,0.0004047413,0.00001113818,0.00008186673,0.00002215122,0.0002198323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06366384,"threshold_uncertainty_score":0.4230686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1593472197911574,"score_gpt":0.4538938274257359,"score_spread":0.2945466076345785,"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."}}