{"id":"W4405606501","doi":"10.1371/journal.pcbi.1012632","title":"Predicting lung aging using scRNA-Seq data","year":2024,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; National Heart, Lung, and Blood Institute; National Institute on Aging; National Institutes of Health","keywords":"Lung; Representation (politics); Regression analysis; Computational biology; Regression; Predictive modelling; Computer science; Bioinformatics; Biology; Medicine; Machine learning; Internal medicine; Statistics; Mathematics","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":[0.0001496036,0.0001165551,0.00009400718,0.00007438585,0.00008715347,0.00004459234,0.0002435206,0.0001021531,0.00003584971],"category_scores_gemma":[0.00008959106,0.0001174468,0.00003967196,0.0001059393,0.00006997065,0.00001086467,0.0003271176,0.00007785991,0.00001589652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003048372,"about_ca_system_score_gemma":0.000171577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000104104,"about_ca_topic_score_gemma":0.000002652337,"domain_scores_codex":[0.9989691,0.00007354802,0.0002015832,0.0004891345,0.00008642449,0.0001802539],"domain_scores_gemma":[0.9995023,0.00005435761,0.00004844925,0.0002741116,0.00006993686,0.00005085348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002360976,0.00005449795,0.02724469,0.0001468917,0.0004080808,0.00001092872,0.00004151485,0.03008451,0.9361165,0.001796053,0.001671217,0.002401576],"study_design_scores_gemma":[0.0001682365,0.00002866216,0.003149973,0.00007488109,0.00006321701,0.00005223773,0.00001092102,0.9847258,0.007250216,0.001504504,0.00278476,0.0001865778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236447,0.004131182,0.07105701,0.0002133225,0.0003097281,0.0001456766,0.0002486738,0.0000692358,0.0001804446],"genre_scores_gemma":[0.984945,0.00001487506,0.009877456,0.0001324465,0.0006764474,0.000004773999,0.004295192,0.00002358055,0.00003020625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9546413,"threshold_uncertainty_score":0.4789341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04167301305750342,"score_gpt":0.3241089488048556,"score_spread":0.2824359357473522,"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."}}