:
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.
In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion
