With the rapid development of research on the human gut microbiome,associations between the microbiome and various complex chronic diseases have been revealed(Bergot et al.,2019).These advancements provide great oppor...With the rapid development of research on the human gut microbiome,associations between the microbiome and various complex chronic diseases have been revealed(Bergot et al.,2019).These advancements provide great opportunities for studying the roles of the microbiome in disease prediction(Kashyap et al.,2017).展开更多
With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been gained.However,there is still much more to learn about microbial communities,including...With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been gained.However,there is still much more to learn about microbial communities,including billions of novel species and genes,as well as countless spatiotemporal dynamic patterns within the microbial communities,which together form the microbial dark matter.In this work,we summarized the dark matter in microbiome research and reviewed current data mining methods,especially artificial intelligence(AI)methods,for different types of knowledge discovery from microbial dark matter.We also provided case studies on using AI methods for microbiome data mining and knowledge discovery.In summary,we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore,with the goal of advancing our understanding of microbial communities,as well as developing better solutions to global concerns about human health and the environment.展开更多
基金This work was partially supported by the National Natural Science Foundation of China(32071465,31871334,and 31671374)the National Key R&D Program(2018YFC0910502).
文摘With the rapid development of research on the human gut microbiome,associations between the microbiome and various complex chronic diseases have been revealed(Bergot et al.,2019).These advancements provide great opportunities for studying the roles of the microbiome in disease prediction(Kashyap et al.,2017).
基金partially supported by the National Natural Science Foundation of China(Grant Nos.32071465,31871334,and 31671374)the National Key R&D Program of China(Grant No.2018YFC0910502).
文摘With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been gained.However,there is still much more to learn about microbial communities,including billions of novel species and genes,as well as countless spatiotemporal dynamic patterns within the microbial communities,which together form the microbial dark matter.In this work,we summarized the dark matter in microbiome research and reviewed current data mining methods,especially artificial intelligence(AI)methods,for different types of knowledge discovery from microbial dark matter.We also provided case studies on using AI methods for microbiome data mining and knowledge discovery.In summary,we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore,with the goal of advancing our understanding of microbial communities,as well as developing better solutions to global concerns about human health and the environment.