{"id":"W4406164867","doi":"10.1021/acsnano.4c12259","title":"AI-Based Prediction of Protein Corona Composition on DNA Nanostructures","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Institute of General Medical Sciences; Translational Impacts; National Institutes of Health; Division of Chemical, Bioengineering, Environmental, and Transport Systems; Wellcome Trust; McKnight Foundation; BRAIN Foundation; University of California, San Diego; Alfred P. Sloan Foundation; University of California Berkeley; Burroughs Wellcome Fund; Simons Foundation; National Science Foundation; Camille and Henry Dreyfus Foundation; Philomathia Foundation; Gordon and Betty Moore Foundation; Helen Wills Neuroscience Institute, University of California Berkeley","keywords":"Nanostructure; Nanotechnology; DNA; DNA origami; DNA nanotechnology; Materials science; Biophysics; Computational biology; Chemistry; Biology; Biochemistry","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":[0.0002583966,0.0002663245,0.0002292642,0.0005017978,0.00009795807,0.000228772,0.0001513297,0.0003980499,0.0003520479],"category_scores_gemma":[0.0009028014,0.0001301167,0.000232238,0.00017074,0.0001136936,0.0002503357,0.00008922877,0.0002262952,0.0001652956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308038,"about_ca_system_score_gemma":0.0002045089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002029432,"about_ca_topic_score_gemma":0.002199088,"domain_scores_codex":[0.9999224,0.00001265279,0.00000506854,0.00003215129,0.0000191516,0.000008508884],"domain_scores_gemma":[0.9997036,0.000153091,0.00004989166,0.00001220231,0.00006427285,0.00001691772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005233667,0.0001684743,0.02343082,0.0001149157,0.00007507825,0.0001129083,0.00004698044,0.1923018,0.7255533,0.0003883862,0.0003576448,0.0569263],"study_design_scores_gemma":[0.000004594188,0.00003987275,0.004407207,0.000002305826,0.000009909241,0.00002773693,0.000006729245,0.9289846,0.06629579,0.0001066963,0.0001078849,0.000006650327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9147685,0.0002991599,0.08275926,0.00007787325,0.00001339289,0.00004013345,0.0002568434,0.0006386255,0.001146266],"genre_scores_gemma":[0.9677457,0.0001187807,0.03133545,0.00003561997,0.000004316449,0.00002141666,0.0002258792,0.00002169723,0.0004911967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002029432,"threshold_uncertainty_score":0.004035234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004933151810829255,"score_gpt":0.2530892833068406,"score_spread":0.2481561314960113,"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."}}