Human Age Estimation from Gene Expression Data using Artificial Neural Networks

TL;DR

This paper introduces a novel neural network-based framework for human age estimation from gene expression data, utilizing a new spatial representation and data augmentation to improve prediction accuracy over existing methods.

Contribution

The study presents a new spatial representation and data augmentation approach for gene expression data, combined with an ensemble neural network architecture for more accurate age prediction.

Findings

Proposed framework outperforms existing DNA methylation and gene expression methods.

New spatial representation enhances data interpretability.

Ensemble neural network improves age estimation accuracy.

Abstract

The study of signatures of aging in terms of genomic biomarkers can be uniquely helpful in understanding the mechanisms of aging and developing models to accurately predict the age. Prior studies have employed gene expression and DNA methylation data aiming at accurate prediction of age. In this line, we propose a new framework for human age estimation using information from human dermal fibroblast gene expression data. First, we propose a new spatial representation as well as a data augmentation approach for gene expression data. Next in order to predict the age, we design an architecture of neural network and apply it to this new representation of the original and augmented data, as an ensemble classification approach. Our experimental results suggest the superiority of the proposed framework over state-of-the-art age estimation methods using DNA methylation and gene expression data.