Xiangdong Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Xiangdong Zhou has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Keith C. C. Chan
Department of Computing, The Hong Kong Polytechnic University, Hong Kong
2 shared publications Jingbin Wang
College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China
1 shared publicationKeith C. C. Chan
Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, P. R. China
1 shared publicationZhihua Huang
College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China
1 shared publication
Latest Publications
- 15 October 2020
- Published by World Scientific Pub Co Pte Ltd in Journal of Bioinformatics and Computational Biology
- Vol. 18 (05) , 2050035
Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease
As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.- 15 October 2020
- Published by World Scientific Pub Co Pte Ltd in Journal of Bioinformatics and Computational Biology
- Vol. 18 (05) , 2050035
Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease
As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.- Open Access
- 18 September 2018
- Published by Springer Nature in BMC Bioinformatics
- Vol. 19 (1) , 329-329:13
Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification
Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.
- 18 September 2018
- Published by Springer Nature in BMC Bioinformatics
- Vol. 19 (1) , 329-329:13
Open AccessDetecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification
Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.
- 2 January 2017
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy
Multifactor dimensionality reduction (MDR) is originally proposed to identify gene-gene and gene-environment interactions associated with binary traits. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these methods are still not computationally efficient or effective. In this paper, we propose Fuzzy Quantitative trait based Ordinal MDR (QOMDR) to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then using a fuzzy balance accuracy measure based on generalized member function of fuzzy sets to select best sets of SNPs as having strong association with the trait. Experimental results on two real datasets show that our algorithm has better consistency and classification accuracy in identifying gene-gene interactions associated with QTs.
- 2 January 2017
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy
Multifactor dimensionality reduction (MDR) is originally proposed to identify gene-gene and gene-environment interactions associated with binary traits. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these methods are still not computationally efficient or effective. In this paper, we propose Fuzzy Quantitative trait based Ordinal MDR (QOMDR) to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then using a fuzzy balance accuracy measure based on generalized member function of fuzzy sets to select best sets of SNPs as having strong association with the trait. Experimental results on two real datasets show that our algorithm has better consistency and classification accuracy in identifying gene-gene interactions associated with QTs.
- 20 December 2009
- Published by China Science Publishing & Media Ltd. in Journal of Computer Applications
- Vol. 29 (12) , 3256-3258
Enhancement of topology preservation of self-organizing map
- 20 December 2009
- Published by China Science Publishing & Media Ltd. in Journal of Computer Applications
- Vol. 29 (12) , 3256-3258
Enhancement of topology preservation of self-organizing map