Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model

30Citations
Citations of this article
33Readers
Mendeley users who have this article in their library.

Abstract

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and $k$k-Fold Cross Validation ($k$k-Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 $\pm$± 0.0009 and 0.8955 $\pm$± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.

References Powered by Scopus

The Human Microbiome Project

4467Citations
N/AReaders
Get full text

Metagenomic analysis of the human distal gut microbiome

3833Citations
N/AReaders
Get full text

Revised Estimates for the Number of Human and Bacteria Cells in the Body

3584Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

26Citations
N/AReaders
Get full text

GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier

19Citations
N/AReaders
Get full text

GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder

18Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Li, H., Wang, Y., Zhang, Z., Tan, Y., Chen, Z., Wang, X., … Wang, L. (2021). Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2502–2513. https://doi.org/10.1109/TCBB.2020.2986459

Readers over time

‘21‘22‘23‘24‘250481216

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 11

85%

Researcher 2

15%

Readers' Discipline

Tooltip

Computer Science 6

67%

Agricultural and Biological Sciences 1

11%

Medicine and Dentistry 1

11%

Engineering 1

11%

Save time finding and organizing research with Mendeley

Sign up for free
0