SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification

TL;DR

SurvODE is a novel neural ODE-based framework that models gene expression distributions over time, enabling early cancer detection by simulating gene expression at unobserved early stages.

Contribution

It introduces a neural ODE integrated with Cox regression to simulate gene expression distributions at any time point, including early cancer stages, outperforming existing methods.

Findings

Substantial improvement over existing approaches on TCGA data

Effective simulation of gene expression at early cancer stages

Potential for early cancer identification through expression distribution modeling

Abstract

With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development. Existing methods have shown encouraging performance in identifying genes that are predictive for cancer survival, but are still limited in modeling the distribution over genes. Here, we proposed a novel method that can simulate the gene expression distribution at any given time point, including those that are out of the range of the observed time points. In order to model the irregular time series where each patient is one observation, we integrated a neural ordinary differential equation (neural ODE) with cox regression into our framework. We evaluated our method on eight cancer types on TCGA and observed a substantial improvement over existing approaches. Our visualization results and further analysis indicate how our method can be used to simulate expression at the early cancer stage, offering the possibility for early cancer identification.