{"id":"W2303047572","doi":"10.1021/jacs.5b12953","title":"Synergy of Two Assembly Languages in DNA Nanostructures: Self-Assembly of Sequence-Defined Polymers on DNA Cages","year":2016,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Fonds Québécois de la Recherche sur la Nature et les Technologies; Research Corporation for Science Advancement","keywords":"Chemistry; DNA; Sequence (biology); Self-assembly; Polymer; Nanostructure; Nanotechnology; DNA sequencing; Computational biology; Combinatorial chemistry; Crystallography; Biochemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002010897,0.0001597357,0.0004067556,0.00003253216,0.00002369983,0.000004520779,0.0003604439,0.00009325636,0.000001127687],"category_scores_gemma":[0.0001702927,0.00008440831,0.000509972,0.0002458423,0.0003650567,0.000005863221,0.00009322301,0.0001360432,8.960619e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006835971,"about_ca_system_score_gemma":0.00009488616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006331059,"about_ca_topic_score_gemma":0.000007031098,"domain_scores_codex":[0.9988443,0.00008620735,0.0004297016,0.0001776651,0.0002662671,0.0001959027],"domain_scores_gemma":[0.9985253,0.0000796749,0.0009302843,0.0002849732,0.0001245284,0.00005529404],"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":[0.00009090302,0.00007031066,0.001076146,0.000009500059,0.0001593433,0.000001871405,0.00004382597,0.000002732458,0.9943951,0.00001251215,0.0005200556,0.003617747],"study_design_scores_gemma":[0.0003908893,0.0002596262,0.0008221832,0.0001015286,0.00005266867,0.00002404637,0.0002258886,0.000003642172,0.997713,0.00006006871,0.0002302404,0.0001162245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988625,0.0001210733,0.00006557829,0.0007838331,0.00002885801,0.00003826527,0.00001982407,0.000007216371,0.00007287487],"genre_scores_gemma":[0.9951307,0.0004068417,0.00395457,0.0003480184,0.0001046931,7.265483e-7,0.000002182445,0.00001445336,0.00003776805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003888992,"threshold_uncertainty_score":0.3442071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00641707773670661,"score_gpt":0.2783588617244914,"score_spread":0.2719417839877847,"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."}}