Deep biomarkers of human aging: Application of deep neural networks to biomarker development

By E. Putin, Polina Mamoshina, Alexander M. Aliper, Mikhail Korzinkin, A. Moskalev, A. Kolosov, A. Ostrovskiy, Charles R. Cantor, J. Vijg, A. Zhavoronkov. Aging, 2016

350 citations recorded by the source corpus.

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R2 = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R2 = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

Subfields: Aging, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/0188e8d525e525b743ef89d96727a342dfaa4480

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Polina Mamoshina
Daniele Ravì
Lucy Ojomoko
Javier Andreu-Perez
Artem V. Artemov
Yury Yanovich
Alex Aliper
Charence Wong

Citations in the corpus

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015, methodology): “For example, an ensemble of neural networks was applied to predict age and sex of patients based on their common blood test profiles [36].”
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes (2016, background): “Deep-learned systems have demonstrated reasonable accuracy when guessing patients’ chronological age using anonymized blood biochemistry samples (Putin et al. 2016) .”
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. (2017, background): “[7, 8], drug discovery [4, 9, 10], predicting clinical trial outcomes [11], and generating”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “However, recent advancements in IS are permeating almost every aspect of our life with the introduction of intelligent transportation [1-10], intelligent diagnosis and health monitoring for precision medicine [11-14], robotics and automation in home appliances [15], virtual online assistance [16], e”
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016, background): “…of this flexibility and adaptability of DNN for learning from large range of data, DNNs are now considered as an increasingly important area in the biomedical field that shows significant potential in comprehensive -omics analysis and could be useful for tackling many current issues [1, 8, 9].”
Deep Learning for Health Informatics (2017, methodology): “[108] applied DNNs for identifying markers that predict human chronological age based on simple blood tests.”
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018, neutral): “The exponential growth of publicly available experimental data also increases popularity of machine learning applications in bioinformatics [9, 10] for predicting pharmacological properties of drugs [11], biomarker development [12–15], and target identiﬁcation [16].”