Microbes and complex diseases: from experimental results to computational models

Abstract

Abstract Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe–disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.