Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data

07/12/2018
by   Emma Pierson, et al.
4

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual only observed at a single timepoint, making inference of temporal dynamics hard. Motivated by the study of human aging, we present a model that can learn temporal dynamics from cross-sectional data. Our model represents each individual with a low-dimensional latent state that consists of 1) a dynamic vector rt that evolves linearly with time t, where r is an individual-specific "rate of aging" vector, and 2) a static vector b that captures time-independent variation. Observed features are a non-linear function of rt and b. We prove that constraining the mapping between rt and a subset of the observed features to be order-isomorphic yields a model class that is identifiable if the distribution of time-independent variation is known. Our model correctly recovers the latent rate vector r in realistic synthetic data. Applied to the UK Biobank human health dataset, our model accurately reconstructs the observed data while learning interpretable rates of aging r that are positively associated with diseases, mortality, and aging risk factors.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/22/2016

LFADS - Latent Factor Analysis via Dynamical Systems

Neuroscience is experiencing a data revolution in which many hundreds or...
research
06/15/2020

Learning Latent Space Energy-Based Prior Model

The generator model assumes that the observed example is generated by a ...
research
04/18/2019

A New Class of Time Dependent Latent Factor Models with Applications

In many applications, observed data are influenced by some combination o...
research
12/27/2018

Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

This paper studies the dynamic generator model for spatial-temporal proc...
research
10/07/2020

Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences

Time series sequence prediction and modelling has proven to be a challen...
research
03/02/2022

Understanding microbiome dynamics via interpretable graph representation learning

Large-scale perturbations in the microbiome constitution are strongly co...
research
06/05/2023

PV Fleet Modeling via Smooth Periodic Gaussian Copula

We present a method for jointly modeling power generation from a fleet o...

Please sign up or login with your details

Forgot password? Click here to reset