VEGN: Variant Effect Prediction with Graph Neural Networks

06/25/2021
by   Jun Cheng, et al.
6

Genetic mutations can cause disease by disrupting normal gene function. Identifying the disease-causing mutations from millions of genetic variants within an individual patient is a challenging problem. Computational methods which can prioritize disease-causing mutations have, therefore, enormous applications. It is well-known that genes function through a complex regulatory network. However, existing variant effect prediction models only consider a variant in isolation. In contrast, we propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants. The graph is created by assigning variants to genes and connecting genes with an gene-gene interaction network. In this context, we explore an approach where a gene-gene graph is given and another where VEGN learns the gene-gene graph and therefore operates both on given and learnt edges. The graph neural network is trained to aggregate information between genes, and between genes and variants. Variants can exchange information via the genes they connect to. This approach improves the performance of existing state-of-the-art models.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/12/2019

Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction

Disease-gene prediction (DGP) refers to the computational challenge of p...
09/09/2021

GNisi: A graph network for reconstructing Ising models from multivariate binarized data

Ising models are a simple generative approach to describing interacting ...
10/26/2020

Expectile Neural Networks for Genetic Data Analysis of Complex Diseases

The genetic etiologies of common diseases are highly complex and heterog...
12/01/2018

Explainable Genetic Inheritance Pattern Prediction

Diagnosing an inherited disease often requires identifying the pattern o...
12/29/2021

A Boolean Algebra for Genetic Variants

Beyond identifying genetic variants, we introduce a set of Boolean relat...
05/08/2020

Predicting gene expression from network topology using graph neural networks

Motivation: It is known that the structure of transcription and protein ...
05/06/2019

Analysis of Gene Interaction Graphs for Biasing Machine Learning Models

Gene interaction graphs aim to capture various relationships between gen...