:
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.
NCPCDA: network consistency projection for circRNA–disease association prediction
