The Bi-Direction Similarity Integration Method for Predicting Microbe-Disease Associations

Abstract

Identification of microbe-disease associations provides insight into the mechanism that microbes cause diseases at the molecular level. Existing microbe–disease association prediction methods mainly utilize microbe–disease association profiles to calculate microbe–microbe similarities and disease–disease similarities, and then build similarity-based prediction models. However, they ignore important biological knowledge, e.g., disease Medical Subject Headings (MeSH), and do not consider unequal contributions of microbe information and disease information. In this paper, we propose the bi-direction similarity integration label propagation (BDSILP) method for predicting microbe–disease associations. First, BDSILP introduces disease MeSH to calculate the disease–disease semantic similarity and the microbe–microbe functional similarity. Although MeSH is not available for all diseases, BDSILP presents a strategy for integrating multiple similarities for microbes and diseases. Second, two graphs are constructed by using integrated disease similarity and integrated microbe similarity, and BDSILP implements the label propagation on the graphs to score microbe–disease pairs. Third, BDSILP adopts the weighted averages of their scores as final predictions. BDSILP produces better performances than existing state-of-the-art methods, achieving the AUC of 0.9131 and the AUPR of 0.5343 in leave-one-out cross validation, and achieving the AUC of 0.9051 and the AUPR of 0.3037 in five-fold cross validation. Moreover, case studies and discussion demonstrate that BDSILP is promising for predicting novel microbe–disease associations.