PBHMDA specifications


Unique identifier OMICS_29680
Alternative name Path-Based Human Microbe-Disease Association
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Zexuan Zhu
  • person_outline Xing Chen

Publication for Path-Based Human Microbe-Disease Association

PBHMDA citations


The Genome Scale Integrated Networks in Microorganisms

Front Microbiol
PMCID: 5829631
PMID: 29527198
DOI: 10.3389/fmicb.2018.00296

[…] They used a graph-based scoring method and neighbor-based collaborative filtering to calculate the possibility of association between microorganisms and diseases (). developed a computational model PBHMDA (Path-Based Human Microorganism-Disease Association prediction) based on the Gaussian interaction profile kernel similarity calculation for microorganisms and diseases. Besides, this model also […]


A novel approach for predicting microbe disease associations by bi random walk on the heterogeneous network

PLoS One
PMCID: 5589230
PMID: 28880967
DOI: 10.1371/journal.pone.0184394

[…] sociation scores obtained from BiRWHMDA. We observed microbes from the top 10 candidate microbes confirmed by current research. Furthermore, we compare the results of BiRWHMDA with the latest method, PBHMDA. In this study, we assume that if a microbe is associated with one disease, the genus that the microorganism belongs to is also associated with the disease.Asthma is a common long-term inflamma […]

PBHMDA institution(s)
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China; School of Life Science, Liaoning University, Shenyang, China; Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
PBHMDA funding source(s)
Supported by National Natural Science Foundation of China under Grant No. 11301517 and 11631014, the National Natural Science Foundation of China under Grant No. 61471246, Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions under Grant No. Yq2013141, and Guangdong Special Support Program of Top-notch Young Professionals under Grant No. 2014TQ01X273, the National Natural Science Foundation of China under Grant No. 31570160 and Innovation Team Project from the Education Department of Liaoning Province under Grant No. LT2015011, the National Natural Science Foundation of China under Grant No. 11371355 and 11631014, the National Natural Science Foundation of China under Grant No. 61572506 and Pioneer Hundred Talents Program of Chinese Academy of Sciences, the National Nature Science Foundation of China under Grant No. 61572328 and research funding of China-UK Visual Information Processing Lab.

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