# Human Age Estimation from Gene Expression Data using Artificial Neural Networks

arXiv: https://arxiv.org/abs/2111.02692 · HTML: https://mlsanity.com/paper/2111.02692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?](https://mlsanity.com/paper/1511.06348/) (2015)
- [Deep Bayesian Active Learning, A Brief Survey on Recent Advances](https://mlsanity.com/paper/2012.08044/) (2020)
- [Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition](https://mlsanity.com/paper/2108.01569/) (2021)
- [Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors](https://mlsanity.com/paper/2103.14096/) (2021)
- [An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering](https://mlsanity.com/paper/2109.08795/) (2021)
- [Detection and Statistical Modeling of Birth-Death Anomaly](https://mlsanity.com/paper/1906.11788/) (2019)
- [A statistical framework for model-based inverse problems in ultrasound elastography](https://mlsanity.com/paper/2010.10729/) (2020)

## Built on by (most cited first, 2 of 2)

- [More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning](https://mlsanity.com/paper/2307.00651/) (2023)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)

## Abstract (arXiv)

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.