{"id":"W4404420577","doi":"10.1093/nsr/nwae410","title":"Integrated multi-omics characterization across clinically relevant subgroups of long COVID","year":2024,"lang":"en","type":"article","venue":"National Science Review","topic":"Mitochondrial Function and Pathology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; Shanghai Municipal Health Commission","keywords":"Coronavirus disease 2019 (COVID-19); Omics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computational biology; Characterization (materials science); Biology; Virology; Medicine; Bioinformatics; Internal medicine; Nanotechnology; Materials science; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001598636,0.00007487812,0.0001308796,0.00003816289,0.00006849087,0.00002757387,0.0001902258,0.00007459159,0.0000713362],"category_scores_gemma":[0.001637131,0.00005903362,0.00007610972,0.000477889,0.0002265923,0.00001248581,0.0000596804,0.00008142109,0.0000388124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003358936,"about_ca_system_score_gemma":0.0006028502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002348131,"about_ca_topic_score_gemma":0.000006056761,"domain_scores_codex":[0.9988735,0.00006013557,0.0003627606,0.0003144131,0.0002593685,0.0001298105],"domain_scores_gemma":[0.9992603,0.0000293646,0.00009566189,0.0001234717,0.0004260547,0.00006513283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001640142,0.00003855532,0.0008791093,0.0003478095,0.000008787711,0.000002202435,0.00002138682,0.000005317643,0.9712266,0.001125659,0.0003268447,0.02600129],"study_design_scores_gemma":[0.0007620507,0.0005781232,0.02020267,0.002307458,0.00006677001,0.0002391695,0.00002689708,0.004308361,0.07693763,0.0002105276,0.8937572,0.0006031815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5046814,0.07009615,0.4061696,0.00661394,0.006748962,0.002381084,0.001024614,0.0001485889,0.002135637],"genre_scores_gemma":[0.8343855,0.1438675,0.009014724,0.007214544,0.0006795333,0.00008244727,0.00248639,0.00003241705,0.002236926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.894289,"threshold_uncertainty_score":0.2407321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04500032676335444,"score_gpt":0.38563309903671,"score_spread":0.3406327722733556,"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."}}