Yu-An Huang1, Zhi-An Huang2, Jian-Qiang Li3, Zhu-Hong You4, Lei Wang5, Hai-Cheng Yi6, Chang-Qing Yu4. 1. Department of Information Engineering, Xijing University, Xi'an, 710123, China. yahuang1991@gmail.com. 2. Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China. 3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China. lijq@szu.edu.cn. 4. Department of Information Engineering, Xijing University, Xi'an, 710123, China. 5. Guangxi Academy of Science, Nanning, 530000, China. 6. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, 830000, China.
Abstract
BACKGROUND: Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS: Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION: Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.
BACKGROUND: Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS: Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION: Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.
Authors: C Manichanh; L Rigottier-Gois; E Bonnaud; K Gloux; E Pelletier; L Frangeul; R Nalin; C Jarrin; P Chardon; P Marteau; J Roca; J Dore Journal: Gut Date: 2005-09-27 Impact factor: 23.059
Authors: M A Larkin; G Blackshields; N P Brown; R Chenna; P A McGettigan; H McWilliam; F Valentin; I M Wallace; A Wilm; R Lopez; J D Thompson; T J Gibson; D G Higgins Journal: Bioinformatics Date: 2007-09-10 Impact factor: 6.937