{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003126885,0.0003935716,0.0004019542,0.0001992689,0.00008949805,0.00009426451,0.0004768975,0.0004868991,0.00002841602],"category_scores_gemma":[0.0001985337,0.0004458499,0.0004307234,0.0002243,0.0001061869,0.000008166852,0.0008276754,0.0003352707,0.00002680463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005758605,"about_ca_system_score_gemma":0.0003370716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001956936,"about_ca_topic_score_gemma":0.00001347987,"domain_scores_codex":[0.997737,0.0001446348,0.0006366177,0.0008764683,0.0002602963,0.0003449756],"domain_scores_gemma":[0.9978473,0.00002076586,0.000448238,0.001210897,0.0003235255,0.0001492545],"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":[0.000009651789,0.000115644,0.01034845,0.000239364,0.0003073548,0.00001701314,0.000006524531,0.0009871818,0.9876827,0.00008230315,0.0002005622,0.000003262931],"study_design_scores_gemma":[0.0002750172,0.00005974599,0.07472053,0.0004675454,0.0001607295,2.060051e-9,0.000002955031,0.0002224664,0.9219605,0.00002720918,0.001652182,0.0004510758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681967,0.004160204,0.02465641,0.0001862395,0.0009088119,0.0005095146,0.001293283,0.00008146912,0.000007399524],"genre_scores_gemma":[0.9948111,0.0007399091,0.003754671,0.00006718144,0.0002856679,0.0001486639,0.0000285825,0.0001363605,0.00002787963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06572215,"threshold_uncertainty_score":0.9997993,"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."}}