{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001930944,0.0003132525,0.0004016451,0.0002640517,0.0001826315,0.0003312032,0.0002758334,0.0004371925,0.00164579],"category_scores_gemma":[0.0002500558,0.0001638127,0.0003835833,0.0002078672,0.0001949328,0.00009422089,0.0002773633,0.0004919349,0.0004832964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000307209,"about_ca_system_score_gemma":0.0002697866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130917,"about_ca_topic_score_gemma":0.001704765,"domain_scores_codex":[0.9997754,0.00001542387,0.00002252438,0.00008824988,0.00006286217,0.00003568802],"domain_scores_gemma":[0.9998963,0.00002583968,0.00002976188,0.00001459079,0.00001158243,0.00002190121],"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":[0.0001366385,0.00002720505,0.00089808,0.00007411344,0.000021702,0.0002689437,0.00001650085,0.0003324526,0.9934089,0.0002165046,0.0002687991,0.004330115],"study_design_scores_gemma":[0.00004537887,0.0002670073,0.01170125,0.00001517126,0.00008851077,0.001394631,0.00004508357,0.009708786,0.9678217,0.0002428307,0.00864563,0.00002401132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723485,0.001637764,0.0186485,0.0002752194,0.00008323283,0.0000955648,0.002881815,0.001213548,0.002815723],"genre_scores_gemma":[0.9862976,0.0005408596,0.008760438,0.0001551059,0.000006648577,0.00005597048,0.001933026,0.00008564223,0.002164522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00164579,"threshold_uncertainty_score":0.005505741,"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."}}