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Deep Neural Networks for Image-Based Dietary Assessment

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Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.

Ping Xuan¹, Yihua Dong², Yahong Guo³

¹School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China. xuanping@hlju.edu.cn.

International Journal of Molecular Sciences

|November 28, 2018

Summary

We developed CNNDMP, a novel dual convolutional neural network method to predict disease-related microRNAs (disease miRNAs). This approach enhances understanding of disease etiology and pathogenesis by capturing deep features of miRNA and disease networks.

Keywords:

convolutional neural network miRNA–disease association network topology structure random walk

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying disease-related microRNAs (disease miRNAs) is crucial for understanding disease etiology and pathogenesis.
Current methods often integrate miRNA/disease similarities and associations but fail to capture deep network features.
There's a need for advanced methods to learn complex relationships within miRNA and disease networks.

Purpose of the Study:

To propose a novel dual convolutional neural network-based method (CNNDMP) for predicting candidate disease miRNAs.
To improve the prediction of disease miRNAs by integrating network topology structures and deep feature learning.
To validate the efficacy of CNNDMP against existing state-of-the-art methods.

Main Methods:

Developed CNNDMP, a dual convolutional neural network framework for disease miRNA prediction.
Integrated miRNA and disease similarities with known associations, incorporating network topology via random walks.
Employed an embedding layer combining biological premises and a dual CNN for deep feature extraction.

Main Results:

CNNDMP demonstrated superior prediction performance compared to several state-of-the-art methods in cross-validation.
The method effectively captures deep features from miRNA and disease network topology.
Case studies on breast, colorectal, and lung cancers highlighted CNNDMP's ability to identify potential disease miRNAs.

Conclusions:

CNNDMP offers a powerful new approach for predicting disease-related microRNAs.
The dual convolutional neural network architecture effectively learns deep features from network structures.
This method advances the understanding of miRNA roles in disease pathogenesis and aids in discovering novel disease biomarkers.