AgeGuess, a Methylomic Prediction Model for Human Ages

Xiaoqian Gao; Shuai Liu; Haoqiu Song; Xin Feng; Meiyu Duan; Lan Huang; Fengfeng Zhou

doi:10.3389/fbioe.2020.00080

AgeGuess, a Methylomic Prediction Model for Human Ages

Front Bioeng Biotechnol. 2020 Mar 10:8:80. doi: 10.3389/fbioe.2020.00080. eCollection 2020.

Authors

Xiaoqian Gao¹, Shuai Liu¹, Haoqiu Song^{1

2}, Xin Feng¹, Meiyu Duan¹, Lan Huang³, Fengfeng Zhou¹

Affiliations

¹ BioKnow Health Informatics Laboratory Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China.
² College of Computer Science, Hubei University of Technology, Wuhan, China.
³ Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China.

Abstract

Aging was a biological process under regulations from both inherited genetic factors and various molecular modifications within cells during the lifespan. Multiple studies demonstrated that the chronological age may be accurately predicted using the methylomic data. This study proposed a three-step feature selection algorithm AgeGuess for the age regression problem. AgeGuess selected 107 methylomic features as the gender-independent age biomarkers and the Support Vector Regressor (SVR) model using these biomarkers achieved 2.0267 in the mean absolute deviation (MAD) compared with the real chronological ages. Another regression algorithm Ridge achieved a slightly better MAD 1.9859 using the same biomarkers. The gender-independent age prediction models may be further improved by establishing two gender-specific models. And it's interesting to observe that there were only two methylation biomarkers shared by the two gender-specific biomarker sets and these two biomarkers were within the two known age-associated biomarker genes CALB1 and KLF14.

Keywords: age prediction; methylomic biomarker; regression; ridge; support vector regressor.