Chen Xing, Huang Yu-An, You Zhu-Hong, Yan Gui-Ying, Wang Xue-Song
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Department of Computing, Hong Kong Polytechnic University, Hong Kong.
Bioinformatics. 2017 Mar 1;33(5):733-739. doi: 10.1093/bioinformatics/btw715.
Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now.
In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 ± 0.0054, 0.8301 ± 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement.
Matlab codes and dataset explored in this work are available at http://dwz.cn/4oX5mS .
xingchen@amss.ac.cn or zhuhongyou@gmail.com or wangxuesongcumt@163.com.
Supplementary data are available at Bioinformatics online.
越来越多的临床观察表明，生活在人体中的微生物与多种人类非传染性疾病密切相关，这为理解复杂的疾病机制提供了有前景的见解。预测微生物与疾病的关联不仅可以促进人类疾病的诊断和预后，还能改善新药开发。然而，迄今为止，在大规模理解和预测人类微生物与疾病的关联方面几乎没有做过什么努力。
在这项工作中，我们构建了一个微生物-人类疾病关联网络，并基于功能相似的微生物往往与非传染性疾病具有相似的相互作用和非相互作用模式这一假设，进一步开发了一种用于人类微生物-疾病关联预测的KATZ度量的新型计算模型（KATZHMDA），反之亦然。据我们所知，KATZHMDA是首个用于微生物-疾病关联预测的工具。可靠的预测性能可归因于KATZ度量的使用，以及对微生物和疾病引入高斯相互作用轮廓核相似性。基于从HMDAD数据库获得的已知微生物-疾病关联，实施留一法交叉验证（LOOCV）和k折交叉验证来评估这种新型计算模型的有效性。结果，KATZHMDA在2折和5折交叉验证以及LOOCV框架下分别取得了可靠的性能，平均AUC分别为0.8130±0.0054、0.8301±0.0033和0.8382。预计KATZHMDA可用于获得更多与重要人类非传染性疾病相关的新型微生物，从而有益于药物发现和人类医学进步。
本工作中探索的Matlab代码和数据集可在http://dwz.cn/4oX5mS获取。
xingchen@amss.ac.cn或zhuhongyou@gmail.com或wangxuesongcumt@163.com。
补充数据可在《生物信息学》在线获取。
