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Integrating multi-network topology for gene function prediction using deep neural networks.

Jiajie Peng¹, Hansheng Xue², Zhongyu Wei³

¹School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.

Briefings in Bioinformatics

|April 7, 2020

Summary

This study introduces DeepMNE-CNN, a novel method for gene function prediction that integrates multiple biological networks. The approach effectively captures shared information, outperforming existing methods in annotating gene functions.

Keywords:

function prediction multiple networks network embedding

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Biological networks, such as gene and protein interaction networks, are crucial for understanding complex biological systems.
High-throughput techniques have led to an abundance of biological networks, offering rich data for inferring gene and protein functions.
Existing network embedding methods for gene function prediction do not fully leverage shared information across multiple heterogeneous networks.

Purpose of the Study:

To develop a novel semi-supervised autoencoder method for integrating multiple biological networks.
To generate a low-dimensional feature representation that accounts for correlations among networks.
To improve gene function prediction accuracy by utilizing integrated network features.

Main Methods:

A semi-supervised autoencoder was designed to integrate multiple heterogeneous biological networks.
Shared information and correlations among networks were explicitly considered during feature learning.
A convolutional neural network (CNN) was employed for gene function annotation using the integrated feature embeddings.

Main Results:

The proposed method, DeepMNE-CNN, demonstrated superior performance in gene function prediction on yeast and human datasets compared to three state-of-the-art methods.
The integrated feature representation effectively captured shared information across networks, leading to improved prediction accuracy.
The study provides a comprehensive performance analysis and a reusable tool for gene feature extraction.

Conclusions:

The novel semi-supervised autoencoder approach effectively integrates multiple biological networks for enhanced gene function prediction.
DeepMNE-CNN offers a significant advancement over existing methods by considering inter-network correlations.
The developed tool facilitates downstream machine learning tasks in biological data analysis.