Conference proceeding
Predicting Microbe-Disease Association by Kernelized Bayesian Matrix Factorization
INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II, v 10955, pp 389-394
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The study of microbe-disease associations can be utilized as a valuable material for understanding disease pathogenesis. Developing a highly accurate algorithm model for predicting disease-related microbes will provide a basis for targeted treatment of the disease. In this paper, we propose an approach based on Kernelized Bayesian Matrix Factorization (KBMF) to predict microbe-disease association, based on the Gaussian interaction profile kernel similarity for microbes and diseases. The prediction performance of the method was evaluated by five-fold cross validation. KBMF achieved reliable results which is better than several state-of-the-art methods with around 8% improvement of AUC. Furthermore, case studies have demonstrated the reliability of the method.
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Details
- Title
- Predicting Microbe-Disease Association by Kernelized Bayesian Matrix Factorization
- Creators
- Sisi Chen - Central China Normal UniversityDan Liu - Central China Normal UniversityJia Zheng - Central China Normal UniversityPingtao Chen - Univ. of Sci. and Tech. of ChinaXiaohua Hu - Central China Normal UniversityXingpeng Jiang - Central China Normal University
- Contributors
- D S Huang (Editor)K H Jo (Editor)X L Zhang (Editor)
- Publication Details
- INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II, v 10955, pp 389-394
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 6
- Grant note
- CCNU16KFY04 / Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE Excellent Doctoral Breeding Project of CCNU 61532008 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000484469700047
- Scopus ID
- 2-s2.0-85052122813
- Other Identifier
- 991019167705804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods