{"id":"W4387783953","doi":"10.1016/j.cell.2023.09.012","title":"Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo","year":2023,"lang":"en","type":"article","venue":"Cell","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of General Medical Sciences; National Institutes of Health; H. Lundbeck A/S; Lundbeckfonden; National Eye Institute; VitreoRetinal Surgery Foundation; Research to Prevent Blindness","keywords":"Biology; Proteomics; Disease; Cell; Cell type; Macular degeneration; Computational biology; Transcriptome; Neuroscience; Retinal; Bioinformatics; Pathology; Genetics; Gene; Gene expression; Medicine","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":[0.0005178627,0.0005492417,0.0004283838,0.001131573,0.0004582413,0.001024518,0.0003750998,0.0007260522,0.001982212],"category_scores_gemma":[0.0004899316,0.0002639038,0.0003339843,0.0005444626,0.0003327334,0.0006593588,0.0004360993,0.0009499228,0.0009971609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003246292,"about_ca_system_score_gemma":0.0003203743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341415,"about_ca_topic_score_gemma":0.002682504,"domain_scores_codex":[0.9996787,0.00004605677,0.00001455789,0.0000973681,0.0001002933,0.00006296271],"domain_scores_gemma":[0.9997415,0.0000552317,0.0000517339,0.00003306091,0.00006841507,0.00005004242],"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":[0.0002200337,0.00005456723,0.006005715,0.00005075638,0.00003526689,0.0001078616,0.00004200558,0.00007041069,0.9866822,0.0002352663,0.0002772789,0.006218619],"study_design_scores_gemma":[0.00003239101,0.000455865,0.09132788,0.00003246932,0.0001797452,0.001631387,0.0003620553,0.01051389,0.888579,0.001448925,0.005394776,0.00004169402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130581,0.006169896,0.07083235,0.0006590055,0.0002338987,0.0001232147,0.002056011,0.001033266,0.005834205],"genre_scores_gemma":[0.9550686,0.002412888,0.03495737,0.0007069178,0.0001193759,0.0001409663,0.001424736,0.000186656,0.004982478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001982212,"threshold_uncertainty_score":0.006631136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006793906641585929,"score_gpt":0.2426276258651501,"score_spread":0.2358337192235642,"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."}}