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Current Protein & Peptide Science


ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Research Article

EHAI: Enhanced Human Microbe-Disease Association Identification

Author(s): Ruizhi Fan, Chenhua Dong, Hu Song, Yixin Xu, Linsen Shi, Teng Xu, Meng Cao, Tao Jiang and Jun Song*

Volume 21 , Issue 11 , 2020

Page: [1078 - 1084] Pages: 7

DOI: 10.2174/1389203721666200702150249

Price: $65


Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

Keywords: Diseases, microbe, colorectal cancer, machine learning, EHAI, HMDAD.

Graphical Abstract
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