title: A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.
abstract: 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.
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hong Kong.
authors.forename: Yu-An
authors.initials: YA
authors.lastname: Huang
authors.affiliation: Chinese Academy of Science, Xinjiang Technical Institute of Physics and Chemistry, Ürümqi 830011, China.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Gui-Ying
authors.initials: GY
authors.lastname: Yan
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Xue-Song
authors.initials: XS
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001419:Bacteria; D018407:Bacterial Physiological Phenomena; D019295:Computational Biology / Q000379:methods; D004194:Disease; D054884:Host-Pathogen Interactions; D006801:Humans; D064307:Microbiota / Q000502:physiology*; D008954:Models, Biological