{"id":"W3131596152","doi":"10.1016/j.cell.2021.01.033","title":"DNA origami signposts for identifying proteins on cell membranes by electron cryotomography","year":2021,"lang":"en","type":"article","venue":"Cell","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Medical Research Council; Canadian Institutes of Health Research; Wellcome Trust","keywords":"Biology; DNA origami; Cryo-electron tomography; DNA; Biophysics; Cell biology; Lipid bilayer fusion; Electron tomography; Membrane; Cryo-electron microscopy; Computational biology; Membrane protein; Nanotechnology; Biochemistry; Tomography","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":[0.00010435,0.0001895405,0.0001684587,0.00004876871,0.0001206459,0.00004277074,0.0001285373,0.0001712292,0.000003188272],"category_scores_gemma":[0.00002427469,0.0001757278,0.0002246314,0.0001650476,0.00004331471,0.000003500719,0.00004176592,0.00008941867,0.000003639004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001294834,"about_ca_system_score_gemma":0.00004234744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003327504,"about_ca_topic_score_gemma":0.00001027293,"domain_scores_codex":[0.9988578,0.00004505133,0.0001781299,0.0004967797,0.0001197127,0.0003025365],"domain_scores_gemma":[0.9993447,0.00001554135,0.00009476831,0.000348962,0.0001312454,0.0000647564],"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":[0.00005291273,0.0001519519,0.00002116629,0.00007562593,0.00002827836,0.000003910502,0.000006994527,0.000002100186,0.9947792,0.000005484218,0.004529385,0.0003429624],"study_design_scores_gemma":[0.0003138978,0.0003251632,0.000007593917,0.00001557828,0.00004431872,0.000002665937,0.00002395826,0.00001069912,0.9327593,0.0001168013,0.06615372,0.0002263087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853418,0.002734058,0.008140088,0.0001533132,0.00009914602,0.0005067746,0.00006850147,0.00008733387,0.002868949],"genre_scores_gemma":[0.990171,0.0003786556,0.004313445,0.0003502504,0.0001475891,0.00004469774,0.0004966504,0.00003396006,0.004063707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06201993,"threshold_uncertainty_score":0.716597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008785501320865006,"score_gpt":0.2630487850067922,"score_spread":0.2542632836859272,"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."}}