Abstract
BackgroundAccumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases. Identifying potential microbe-disease associations, which could provide better understanding of disease pathology and further boost disease diagnostic and prognostic, has attracted more and more attention. However, hardly any computational models have been developed for large scale microbe-disease association prediction.ResultsIn this article, based on the assumption that microbes with similar functions tend to share similar association or non-association patterns with similar diseases and vice versa, we proposed the model of Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA) by integrating known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. NCPHMDA yielded outstanding AUCs of 0.9039, 0.7953 and average AUC of 0.8918 in global leave-one-out cross validation, local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, colon cancer, asthma and type 2 diabetes were taken as independent case studies, where 9, 9 and 8 out of the top 10 predicted microbes were successfully confirmed by recent published clinical literature.ConclusionNCPHMDA is a non-parametric universal network-based method which can simultaneously predict associated microbes for investigated diseases but does not require negative samples. It is anticipated that NCPHMDA would become an effective biological resource for clinical experimental guidance.
Highlights
Accumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases
In this paper, based on the assumption that microbes with similar functions tend to share similar association or non-association patterns with similar diseases, we developed the model of Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA) to uncover potential microbedisease associations
Performance evaluation We implemented LOOCV and 5-fold CV on the experimentally verified microbe-disease associations recorded in Human Microbe-Disease Association Database (HMDAD) database to evaluate the prediction performance of NCPHMDA
Summary
Accumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases. Hardly any computational models have been developed for large scale microbe-disease association prediction. In the past few decades, accumulating evidence has demonstrated that human lives strongly rely on a diverse, complex and dynamic microbial community, including bacteria, protozoa, viruses, eukaryotes, archea and so on [1]. Essential gut bacteria could effectively promote nutrient absorption by assisting decomposition of indigestible polysaccharides and production of indispensable vitamins [3]. They provide important protection against invasion of foodborne pathogens by impacting on proliferation and differentiation of host intestinal epithelium [6, 7]. A system understanding of how these biochemical activities achieve still remain largely unknown
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