2018
DOI: 10.3389/fgene.2018.00242
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Abstract: For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differ… Show more

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Cited by 156 publications
(111 citation statements)
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“…Indeed, since 2013, many aging clocks have been developed in both humans and other model organisms. The published aging clocks utilizing deep learning were developed using standard clinical blood tests 42 , facial images 43 , physical activity data, 44 and transcriptomic data 45 . These clocks were used to rank the most important features contributing to the accuracy of the prediction by using the permutation feature importance (PFI), deep feature selection (DFS) and other techniques.…”
Section: Introductionmentioning
confidence: 99%
“…Indeed, since 2013, many aging clocks have been developed in both humans and other model organisms. The published aging clocks utilizing deep learning were developed using standard clinical blood tests 42 , facial images 43 , physical activity data, 44 and transcriptomic data 45 . These clocks were used to rank the most important features contributing to the accuracy of the prediction by using the permutation feature importance (PFI), deep feature selection (DFS) and other techniques.…”
Section: Introductionmentioning
confidence: 99%
“…More recently, the first muscle tissue‐specific transcriptomic aging clock was proposed by applying supervised machine learning algorithms on gene expression data from young and old healthy donors (Table S1, Supporting Information) [ 117 ] ; several candidate biomarkers of human muscle aging, including those which were not yet studied in this context, were reported.…”
Section: Omics‐based Molecule‐pattern Biomarkersmentioning
confidence: 99%
“…Even though these clocks were developed using traditional 23 machine learning approaches as a linear regression with regularization the results suggest 24 1/10 that gradual changes during aging can be tracked using various data types with reasonable 25 accuracy. 26 Previous studies demonstrated age-associated changes in the transcriptome 27 of model organisms [1] and multiple human tissues [2,3,4]. In 2015, Peters and 28 colleagues performed the massive analysis of transcriptional profiles of aging and used 29 six blood expression profiles (7,074 samples in total) to build a predictor of age with 30 leaving a dataset out as validation [3].…”
Section: Introduction 14mentioning
confidence: 99%
“…In the analysis, 1,497 genes were 32 identified as age-related. In 2018, Mamoshina et al proposed a panel of transcriptomic 33 age predictors and the approach of comparing different methods of selecting age-related 34 genes [4]. A deep neural network was the most accurate age predictor showing the 35 accuracy of 0.91 in terms of Pearson correlation and mean absolute error of 6.14 years.…”
Section: Introduction 14mentioning
confidence: 99%
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