{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001092248,0.00007968612,0.0001935035,0.0001704566,0.00002708262,0.00001418093,0.00004124747,0.00001817363,0.00001704308],"category_scores_gemma":[0.0000263878,0.0000647529,0.00004069873,0.0003332243,0.00009534186,0.00004470054,0.00003151258,0.00008431407,0.000007952699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001222856,"about_ca_system_score_gemma":0.00004016493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006614161,"about_ca_topic_score_gemma":0.000002456882,"domain_scores_codex":[0.9994291,0.00001799167,0.0001288612,0.0001622741,0.0001320094,0.0001297437],"domain_scores_gemma":[0.999623,0.00001568699,0.00004770558,0.0001553943,0.00004286208,0.0001153372],"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":[0.0003437025,0.00009259755,0.3605969,0.0007133538,0.00003682006,0.0004315495,0.0009255424,0.0001650353,0.6362604,0.00001188827,0.0004009089,0.00002123825],"study_design_scores_gemma":[0.004143682,0.0004778751,0.06179819,0.001109167,0.0006594825,0.00000342631,0.002398519,0.01860767,0.9098426,0.0002610211,0.0003362893,0.0003620708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981106,0.00008489032,0.0001438072,0.001175902,0.00001918711,0.0001459074,0.000003576601,0.00003482806,0.000281312],"genre_scores_gemma":[0.9953592,0.00004704012,0.000175688,0.00006166885,0.00001540169,0.00000610452,0.00001377196,0.00001224791,0.004308819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2987987,"threshold_uncertainty_score":0.2640547,"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."}}