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学者姓名：周向东
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Abstract ：
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 n 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.
Keyword ：
Complex diseases Complex diseases gene-gene interaction gene-gene interaction interaction group interaction group mutual information mutual information
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. C. , Huang, Zhihua et al. Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease [J]. | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY , 2020 , 18 (5) . |
| MLA | Zhou, Xiangdong et al. "Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease" . | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 18 . 5 (2020) . |
| APA | Zhou, Xiangdong , Chan, Keith C. C. , Huang, Zhihua , Wang, Jingbin . Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease . | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY , 2020 , 18 (5) . |
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Abstract ：
Background: 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. Results: 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. Conclusion: The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.
Keyword ：
Fuzzy accuracy Fuzzy accuracy Gene-gene interactions Gene-gene interactions Multifactor dimensionality reduction Multifactor dimensionality reduction Ordinal traits Ordinal traits Quantitative traits Quantitative traits
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. C. . Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification [J]. | BMC BIOINFORMATICS , 2018 , 19 . |
| MLA | Zhou, Xiangdong et al. "Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification" . | BMC BIOINFORMATICS 19 (2018) . |
| APA | Zhou, Xiangdong , Chan, Keith C. C. . Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification . | BMC BIOINFORMATICS , 2018 , 19 . |
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Abstract ：
Multiple correlated phenotypes often appear in complex traits or complex diseases. These correlated phenotypes are useful in identifying gene-gene interactions associated with complex traits or complex disease more effectively. Some approaches have been proposed to use correlation among multiple phenotypes to identify gene-gene interactions that are common to multiple phenotypes. However these approaches either didn't find truly gene-gene interactions or got results which are hard to explain, especially by using all correlated phenotypes to identify gene-gene interactions, they made identified interactions unreliable. In this paper, we propose Multivariate Quantitative trait based Ordinal MDR (MQOMDR) algorithm to effectively identify gene-gene interactions associated with multiple correlated phenotypes by selecting the best classifier according to not only the training accuracy of the phenotype under consideration but also other phenotypes with weights determined mainly by their pair correlation with the phenotype under consideration and also by repeated selection process to make use of truly useful correlated phenotypes . Experimental results on two real datasets show that our algorithm has better performance in identifying gene-gene interactions associated with multiple correlated phenotypes.
Keyword ：
gene-gene interactions gene-gene interactions multifactor dimensionality reduction multifactor dimensionality reduction Multiple correlated phenotypes Multiple correlated phenotypes ordinal traits ordinal traits quantitative traits quantitative traits
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. , Zhu, Danhong . A Multi-stage Approach to Detect Gene-gene Interactions Associated with Multiple Correlated Phenotypes [C] . 2017 : 279-286 . |
| MLA | Zhou, Xiangdong et al. "A Multi-stage Approach to Detect Gene-gene Interactions Associated with Multiple Correlated Phenotypes" . (2017) : 279-286 . |
| APA | Zhou, Xiangdong , Chan, Keith C. , Zhu, Danhong . A Multi-stage Approach to Detect Gene-gene Interactions Associated with Multiple Correlated Phenotypes . (2017) : 279-286 . |
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Abstract ：
Multiple correlated phenotypes often appear in complex traits or complex diseases. These correlated phenotypes are useful in identifying gene-gene interactions associated with complex traits or complex disease more effectively. Some approaches have been proposed to use correlation among multiple phenotypes to identify gene-gene interactions that are common to multiple phenotypes. However these approaches either didn't find truly gene-gene interactions or got results which are hard to explain, especially by using all correlated phenotypes to identify gene-gene interactions, they made identified interactions unreliable. In this paper, we propose Multivariate Quantitative trait based Ordinal MDR (MQOMDR) algorithm to effectively identify gene-gene interactions associated with multiple correlated phenotypes by selecting the best classifier according to not only the training accuracy of the phenotype under consideration but also other phenotypes with weights determined mainly by their pair correlation with the phenotype under consideration and also by repeated selection process to make use of truly useful correlated phenotypes. Experimental results on two real datasets show that our algorithm has better performance in identifying gene-gene interactions associated with multiple correlated phenotypes. © 2017 IEEE.
Keyword ：
Artificial intelligence Artificial intelligence Bioinformatics Bioinformatics Genes Genes
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. C. , Zhu, Danhong . A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes [C] . 2017 . |
| MLA | Zhou, Xiangdong et al. "A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes" . (2017) . |
| APA | Zhou, Xiangdong , Chan, Keith C. C. , Zhu, Danhong . A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes . (2017) . |
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Abstract ：
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.
Keyword ：
fuzzy accuracy fuzzy accuracy gene-gene interactions gene-gene interactions multifactor dimensionality reduction multifactor dimensionality reduction ordinal traits ordinal traits quantitative traits quantitative traits
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. C. . An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy [C] . 2016 . |
| MLA | Zhou, Xiangdong et al. "An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy" . (2016) . |
| APA | Zhou, Xiangdong , Chan, Keith C. C. . An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy . (2016) . |
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Abstract ：
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. © 2016 IEEE.
Keyword ：
Artificial intelligence Artificial intelligence Association reactions Association reactions Bioinformatics Bioinformatics Classification (of information) Classification (of information) Genes Genes
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C. C. . An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy [C] . 2016 . |
| MLA | Zhou, Xiangdong et al. "An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy" . (2016) . |
| APA | Zhou, Xiangdong , Chan, Keith C. C. . An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy . (2016) . |
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Abstract ：
Gene-gene interaction is an important factor to consider in selecting genes in genotype data or microarray expression data for association with diseases. However current definitions of gene-gene interaction are not very clear and accurate. An inequality is proved in this paper and a new definition of gene-gene interaction: conditional independence and redundancy based definition of gene-gene interaction (CIR), together with a definition of interaction group are proposed according to this inequality. A new algorithm to detect gene-gene interaction with order greater than two is also proposed based on these new definitions and a theorem. Experimental results show the usefulness of these new definitions and the effectiveness and efficiency of this new algorithm. © 2015 IEEE.
Keyword ：
Association reactions Association reactions Bioinformatics Bioinformatics Genes Genes Information theory Information theory
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| GB/T 7714 | Zhou, Xiangdong , Chan, Keith C.C. . A new information-theoretic approach to detect gene-gene interactions in case-control studies [C] . 2015 . |
| MLA | Zhou, Xiangdong et al. "A new information-theoretic approach to detect gene-gene interactions in case-control studies" . (2015) . |
| APA | Zhou, Xiangdong , Chan, Keith C.C. . A new information-theoretic approach to detect gene-gene interactions in case-control studies . (2015) . |
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Abstract ：
在自组织映射(SOM)中,网格各单元的权值向量仅仅是根据各单元和最佳匹配单元(BMU)之间的距离进行更新的,因而输入数据间的拓扑关系不能得到很好的保持.为此提出了两种改进方案.在第一种改进方案中,各单元的权值向量根据各单元和BMU之间对应各坐标的差进行更新.实验结果表明,这种改进方案可以很好地保持拓扑关系,但输入数据的分布密度却不能得到较好的体现.在第二种改进方案中,各单元的权值向量同时根据各单元和BMU之间对应各坐标的差与距离进行更新.实验结果表明,这种改进方案不仅能使拓扑关系得到比SOM更好的保持,而且较好地体现了输入数据的分布密度,并加快了训练的收敛速度.
Keyword ：
分布密度 分布密度 拓扑保持 拓扑保持 最佳匹配单元 最佳匹配单元 权值向量 权值向量 自组织映射 自组织映射
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| GB/T 7714 | 周向东 . 自组织映射拓扑保持的增强 [J]. | 计算机应用 , 2009 , 29 (12) : 3256-3258 . |
| MLA | 周向东 . "自组织映射拓扑保持的增强" . | 计算机应用 29 . 12 (2009) : 3256-3258 . |
| APA | 周向东 . 自组织映射拓扑保持的增强 . | 计算机应用 , 2009 , 29 (12) , 3256-3258 . |
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Abstract ：
通过允许映射对没有被较好映射的输入作较大的调整,无参数自组织映射(PLSOM)能够快速而正确地适应新的输入范围,但是输入分布与权密度之间对应性较差.论文提出了一种基于PLSOM的改进算法.在两种不同的情况下采用两种不同的权值更新方法.一种采用修改过的PLSOM,另一种则采用改进过的sOM.实验结果表明,这种改进算法不仅能快速正确地适应新的输入范围,而且能较好地体现输入分布.
Keyword ：
BMU BMU PLSOM PLSOM SOM SOM 权值密度 权值密度 输入分布 输入分布
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| GB/T 7714 | 周向东 . 一种改进的无参数自组织映射算法 [J]. | 安徽大学学报（自然科学版） , 2009 , 33 (3) : 27-30 . |
| MLA | 周向东 . "一种改进的无参数自组织映射算法" . | 安徽大学学报（自然科学版） 33 . 3 (2009) : 27-30 . |
| APA | 周向东 . 一种改进的无参数自组织映射算法 . | 安徽大学学报（自然科学版） , 2009 , 33 (3) , 27-30 . |
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Abstract ：
在基本进化树算法的基础上,以Visual C++6.0为开发工具,采用改进过的频率敏感竞争学习(FSCL)算法来选择最佳匹配单元(best matching unit,BMU),然后逐级向上修改BMU父结点的权向量直至根结点,从而实现了对进化树的平衡.实验结果表明,该方法不仅大大提高了进化树算法的效率,还减少了聚类误差.
Keyword ：
最佳匹配单元 最佳匹配单元 自组织映射 自组织映射 进化树 进化树 频率敏感竞争学习 频率敏感竞争学习
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| GB/T 7714 | 周向东 . 平衡进化树自组织网络的设计与实现 [J]. | 武汉理工大学学报（信息与管理工程版） , 2009 , 31 (3) : 361-364 . |
| MLA | 周向东 . "平衡进化树自组织网络的设计与实现" . | 武汉理工大学学报（信息与管理工程版） 31 . 3 (2009) : 361-364 . |
| APA | 周向东 . 平衡进化树自组织网络的设计与实现 . | 武汉理工大学学报（信息与管理工程版） , 2009 , 31 (3) , 361-364 . |
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