Human Age Estimation from Gene Expression Data using Artificial Neural Networks
This work addresses age estimation for biomedical research, but it is incremental as it builds on prior studies using gene expression and DNA methylation data.
The authors tackled the problem of predicting human age from gene expression data by proposing a new spatial representation and data augmentation approach, combined with a neural network ensemble, achieving superior performance over state-of-the-art methods.
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.