Abstract
Clinical observations show that human microorganisms get involved in various human biological processes. The disruption of a symbiotic balance for host-microbiota relationship is found to cause different types of human complex diseases. Discoverying the associations between microbes and the host health statuses that they affect could provide great insights into understanding the mechanisms of diseases caused by microbes. However, experimental approaches are time-consuming and expensive. Little effort has been done to develop computational models for predicting pathogenic microbes on a large scale. The prediction results yielded by such models are anticipated to boost the identification and characterization of potential human pathogenic microbes. Based on the assumption that microbes of similar characters tend to get involved in diseases of similar symptoms forming functional clusters, in this paper, we develop a group based computational model of Bayesian disease-oriented ranking for inferring the most potential microbes associated with human diseases. It is the first attempt to predict this kind of associations by using 16S rRNA gene sequences. Based on the sequence information of genes, we use two computational approaches (BLAST+ and MEGA 7) to measure how similar each pairs of microbes are from different aspects. On the other hand, the similarity of diseases is computed based on MeSH descriptors. Using the data collected from HMDAD database, the proposed model achieved AUCs of 0.9456, 0.8266, 0.8866 and 0.8926 in leave-one-out, 2-fold, 5-fold and 10-fold cross validations, respectively. Besides, we conducted a case study on colorectal carcinoma and found that 16 out of top-20 predicted microbes can be confirmed by the published literatures. The prediction result is publicly released and anticipated to help researchers to preferentially validate these promising pathogenic microbe candidates via biological experiments.
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Huang, YA. et al. (2019). Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_13
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DOI: https://doi.org/10.1007/978-3-030-26969-2_13
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