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Your search keyword '"Hsiao H. Tsai"' showing total 2 results
Start Over Author "Hsiao H. Tsai" Topic ensemble classifiers
2 results on '"Hsiao H. Tsai"'

1. Predicting age from the transcriptome of human dermal fibroblasts

Author

Jason G. Fleischer, Roberta Schulte, Hsiao H. Tsai, Swati Tyagi, Arkaitz Ibarra, Maxim N. Shokhirev, Ling Huang, Martin W. Hetzer, and Saket Navlakha

Subjects
Biological age, Skin fibroblasts, Machine learning, Ensemble classifiers, RNA-seq, Aging, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

Published
2018
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2. Predicting age from the transcriptome of human dermal fibroblasts

Author

Ling Huang, Martin W. Hetzer, Saket Navlakha, Swati Tyagi, Arkaitz Ibarra, Jason G. Fleischer, Hsiao H. Tsai, Maxim N. Shokhirev, and Roberta Schulte

Subjects
0301 basic medicine, Adult, Aging, lcsh:QH426-470, Adolescent, Biological age, Short Report, Skin fibroblasts, RNA-Seq, Biology, Bioinformatics, Transcriptome, Machine Learning, 03 medical and health sciences, Young Adult, Progeria, Biomarkers of aging, medicine, Humans, Child, lcsh:QH301-705.5, Aged, Aged, 80 and over, Gene Expression Profiling, Genomics, Biomarker, Fibroblasts, Middle Aged, medicine.disease, Ensemble learning, Human genetics, Biomarker (cell), lcsh:Genetics, 030104 developmental biology, lcsh:Biology (General), Child, Preschool, RNA-seq, Ensemble classifiers
Abstract

Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients. Electronic supplementary material The online version of this article (10.1186/s13059-018-1599-6) contains supplementary material, which is available to authorized users.

Published
2018

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