{"id":"W2158900785","doi":"10.1038/msb.2012.9","title":"Single‐cell analysis of population context advances RNAi screening at multiple levels","year":2012,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"SystemsX.ch; Canadian Institutes of Health Research; Eidgenössische Technische Hochschule Zürich; National Center of Competence in Research Chemical Biology; Universität Zürich; European Commission; Federation of European Biochemical Societies; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Biology; Context (archaeology); RNA interference; Computational biology; Population; Genetics; Gene; RNA; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006959633,0.0003840331,0.0007473757,0.0003227724,0.0002194558,0.000677467,0.0004831791,0.0003818801,0.0005679092],"category_scores_gemma":[0.001389141,0.0003251812,0.0005285738,0.0003230818,0.0004212823,0.0004066138,0.0006035017,0.0008236141,0.0001556721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006043387,"about_ca_system_score_gemma":0.0004843585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933469,"about_ca_topic_score_gemma":0.00308202,"domain_scores_codex":[0.9996248,0.00007094013,0.0000186348,0.000112433,0.0001467861,0.00002640927],"domain_scores_gemma":[0.9989921,0.0005381022,0.0001778608,0.0001494204,0.00009201311,0.00005031484],"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":[0.0001246226,0.00004886258,0.01456965,0.0001255919,0.0001549053,0.000114203,0.0000730302,0.0626495,0.9017426,0.002450438,0.0001137188,0.01783295],"study_design_scores_gemma":[0.00002068626,0.0001577959,0.06176462,0.00001100362,0.0001760842,0.000154731,0.00006678435,0.517716,0.4142698,0.004136629,0.001461313,0.00006451243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7285798,0.0004376268,0.2687489,0.00008734139,0.00001148106,0.00003664045,0.0003616314,0.0004205047,0.001316075],"genre_scores_gemma":[0.9576629,0.0002207796,0.04136035,0.00003255447,0.000005351458,0.00005987336,0.0002972499,0.00007356224,0.000287308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001933469,"threshold_uncertainty_score":0.004384756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443840797709173,"score_gpt":0.2666735016356013,"score_spread":0.2422350936585096,"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."}}