{"id":"W4323076015","doi":"10.1101/2023.02.28.530532","title":"Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; McGill University; McGill University Health Centre","funders":"National Institute on Aging","keywords":"Biological age; Profiling (computer programming); Biological data; Longevity; Computer science; Biology; Biomarker discovery; Computational biology; Bioinformatics; Evolutionary biology; Genetics; Proteomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00418145,0.001866281,0.001578633,0.003459116,0.0004709687,0.00168612,0.001443704,0.0008842819,0.003290983],"category_scores_gemma":[0.0110864,0.0006585625,0.002525035,0.001398419,0.000390919,0.001699806,0.001679123,0.001202274,0.001583372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008398087,"about_ca_system_score_gemma":0.003005534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002640506,"about_ca_topic_score_gemma":0.006062554,"domain_scores_codex":[0.9987541,0.0003138892,0.0001301235,0.000497821,0.0002383886,0.0000656019],"domain_scores_gemma":[0.9964979,0.00190825,0.0004450631,0.0005352827,0.000415363,0.0001980423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004236376,0.0008630092,0.1589453,0.008350731,0.005135704,0.0007945675,0.0005551553,0.1128306,0.06230667,0.01710754,0.06071384,0.5681604],"study_design_scores_gemma":[0.0009897039,0.002424935,0.0435822,0.001109036,0.005844982,0.0005104666,0.0005264566,0.5790596,0.06152868,0.08084591,0.2232523,0.0003257777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2317109,0.03902192,0.4925509,0.005595461,0.001128809,0.002337389,0.157709,0.05433894,0.01560676],"genre_scores_gemma":[0.3766997,0.01261494,0.470414,0.003302759,0.0004864943,0.00295101,0.1263482,0.002220091,0.004962824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00418145,"threshold_uncertainty_score":0.02211386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245174575427885,"score_gpt":0.2762993724437603,"score_spread":0.2517819149009718,"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."}}