{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002201952,0.0001855734,0.0003876783,0.0001692278,0.00005637365,0.000009109226,0.0001789817,0.0002408622,0.00001607275],"category_scores_gemma":[0.00007017193,0.0001708037,0.0002378627,0.0002131979,0.00006371316,0.000009299933,0.0001226226,0.00005138282,0.00001018375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002415072,"about_ca_system_score_gemma":0.000008267082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003619766,"about_ca_topic_score_gemma":0.0001851155,"domain_scores_codex":[0.9986232,0.0002114273,0.0004082267,0.0003241851,0.00009521355,0.0003377403],"domain_scores_gemma":[0.9991047,0.00002471501,0.0002838745,0.000385273,0.0001158041,0.00008560383],"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":[0.00005041213,0.00004705837,0.2146359,0.00001994969,0.0003668243,9.027024e-7,0.00003517615,0.0007036936,0.7824587,0.00004651528,0.00004350192,0.001591379],"study_design_scores_gemma":[0.0006760794,0.0004041241,0.03653485,0.00002410829,0.0003776617,0.00001222571,0.000204668,0.001027902,0.9443758,0.000004127388,0.0159971,0.0003613761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563478,0.01613897,0.02651333,0.00000954278,0.0003376187,0.0001652754,0.00008607868,0.00001088578,0.0003905305],"genre_scores_gemma":[0.998494,0.00005235393,0.0002390773,0.00009829758,0.0001097492,0.0000201514,0.0007487913,0.00001793748,0.0002196159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1781011,"threshold_uncertainty_score":0.6965172,"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."}}