{"id":"W2800773898","doi":"10.1016/j.cell.2018.03.052","title":"An Integrated Genome-wide CRISPRa Approach to Functionalize lncRNAs in Drug Resistance","year":2018,"lang":"en","type":"article","venue":"Cell","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":332,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"Common Fund; National Cancer Institute; Ludwig Center at Harvard; National Institutes of Health; Harvard T.H. Chan School of Public Health; NIH Office of the Director; European Molecular Biology Organization; Università degli Studi di Torino; Fulbright Association; National Human Genome Research Institute; Burroughs Wellcome Fund","keywords":"Biology; GAS6; CRISPR; Gene; Myeloid leukemia; Long non-coding RNA; Computational biology; Genome; Drug resistance; Functional genomics; Genetics; Cancer research; Genomics; RNA; Signal transduction","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":[0.0004662926,0.0004937664,0.0005062246,0.0004526801,0.000408357,0.000849475,0.0007222731,0.0007366678,0.00369468],"category_scores_gemma":[0.0002730427,0.0003290777,0.0007930184,0.0002358462,0.0005070814,0.0004092123,0.0007570632,0.001787978,0.001074852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007202276,"about_ca_system_score_gemma":0.0005294341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007491455,"about_ca_topic_score_gemma":0.002457802,"domain_scores_codex":[0.9995784,0.00005437116,0.00003985683,0.0001316151,0.0001250378,0.00007074591],"domain_scores_gemma":[0.9998348,0.00003399658,0.00003708198,0.0000423298,0.00002159944,0.00003027088],"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":[0.0000419549,0.00004000506,0.00008648133,0.00003072177,0.00001119132,0.00003443482,0.00001029643,0.0001492211,0.9959943,0.0005810392,0.0001516382,0.002868808],"study_design_scores_gemma":[0.00002560627,0.0001858537,0.0008304227,0.000006294687,0.00004435753,0.0002200877,0.00002434622,0.002731319,0.9874422,0.0002719284,0.008202354,0.00001508365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8312013,0.004548985,0.1392873,0.001364731,0.000938284,0.0003988278,0.002128136,0.003335366,0.01679716],"genre_scores_gemma":[0.9503179,0.0008885495,0.03704342,0.0003953633,0.00003956077,0.0001308746,0.001074058,0.0002063936,0.009903982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00369468,"threshold_uncertainty_score":0.01235998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00995856221809219,"score_gpt":0.2580276015382662,"score_spread":0.248069039320174,"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."}}