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A survey on predicting microbe-disease associations: biological data and computational methods.

Zhongqi Wen¹, Cheng Yan², Guihua Duan³

¹Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China.

Briefings in Bioinformatics

|May 22, 2021

Summary

This review systematically examines computational methods for predicting microbe-disease associations (MDAs), offering insights into their performance and suggesting future research directions for improved accuracy in human health studies.

Keywords:

cross validation microbe-disease prediction microbe/disease similarity similarity calculation method

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Area of Science:

Microbiology
Computational Biology
Bioinformatics

Background:

Microbes play a critical role in human disease pathogenesis.
Numerous computational methods exist for predicting microbe-disease associations (MDAs).
A systematic review of these prediction methods is lacking.

Purpose of the Study:

To provide a comprehensive overview of existing computational methods for predicting MDAs.
To classify and detail the algorithms and strategies of these methods.
To evaluate and compare the performance of representative MDA prediction methods.

Main Methods:

Introduction of data sources used in MDA prediction.
Classification of prediction methods based on their nature.
Detailed description of algorithms and strategies.
Experimental evaluation of representative methods using diverse similarity data and calculation approaches.

Main Results:

Comparative analysis of prediction performances across different methods.
Identification of advantages and disadvantages of various computational approaches.
Discussion of current challenges in MDA prediction.

Conclusions:

Suggestions for improving MDA prediction performance.
Future research directions focusing on data, methods, and formulations.
Highlighting the importance of computational approaches in understanding microbe-disease relationships.