Abstract
BackgroundMicrobes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments.MethodsIn this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities.ResultsThe AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature.ConclusionsAll the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.
Highlights
Microbes are closely related to human health and diseases
In this paper, considering some of the above limitations, we developed a novel computational method called MDAKRLS based on the Kronecker regularized least squares method to identify potential microbe-disease associations (MDAs)
The Gaussian interaction profile (GIP) kernel similarity and Hamming interaction profile (HIP) similarity of microbes and diseases should be recalculated in every round of the global leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-CV) framework
Summary
Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. With the fast development of advanced analytical techniques and high-throughput methods for exploring complex microbial communities, in human disease and health, the role of the microbiome has gained widespread attention over the past decade [1, 2]. Microbes are closely related to human health and disease. Most of the gut microbes are either harmless or even beneficial to the human body, such as which can contribute to normal immune function, improve metabolic capability and protect against enteric pathogens [5, 6]. Microbes are considered as “forgotten organs” in host [7]. If the normal balance between the host and microbiota is broken, which may possibly induce many diseases, including asthma [8], inflammatory bowel disease (IBD) [9], brain disorders or neurodevelopmental deficits [10] and even cancer [11], and so on
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