代表性论文（*通讯作者，#共同第一作者）[其他论文见个人主页 https://faculty.sdu.edu.cn/jijiadong/en/zdylm/1339808/list/index.htm]

[1]. Chen H#, Guo Y, He Y, Ji J#, Liu L, Shi Y, Wang Y, Yu L, Zhang X. Simultaneous Differential Network Analysis and Classification for Matrix-variate Data with application to Brain Connectivity. Biostatistics. 2021. doi:10.1093/biostatistics/kxab007. R package "SDNCMV".

[2]. Ji J, He Y, Xie L, Liu L. Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model. Biometrics. 2020. doi.org/10.1111/biom.13359.  R package "MVDN" available at https://github.com/jijiadong/MVDN.

[3]. He Y, Chen H, Sun H, Ji J*, Shi Y*, Zhang X, Liu L. High-dimensional Integrative Copula Discriminant Analysis for Multi-omics Data. Statistics in Medicine. 2020, 39(30): 4869-4884.

[4]. Lin W#, Ji J#, Zhu Y, Li M, Zhao J, Xue F, Yuan Z. PMINR: pointwise mutual information-based network regression – with application to studies of lung cancer and Alzheimer’s disease. Frontiers in genetics. 2020, 11:556259.

[5]. Zhu Y#, Ji J#, Lin W, Li M, Liu L, Zhu H, Xue F, Li X, Zhou X, Yuan Z. MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease. BMC Genetics. 2020, 21:90.

[6]. He Y, Sun H, Ji J*, Zhang X. Robust Feature Screening for Multi-response Trans-elliptical Regression Model with Ultrahigh-dimensional Covariates. Random Matrices: Theory and Applications. 2020, 9(4), 2150001.

[7]. He Y, Zhang L, Ji J*, Zhang X. Robust feature screening for elliptical copula regression model. Journal of Multivariate Analysis. 2019, 173: 568-582.

[8]. Chen H, He Y, Ji J*, Shi Y*. A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction. Frontiers in Neurology. 2019, 10:1162.

[9]. He Y, Ji J*, Xie L, Zhang X, Xue F. A new insight into underlying disease mechanism through semi-parametric latent differential network model. BMC bioinformatics, 2018, 19(17): 493.

[10]. 何勇, 季加东*, 张新生.  Gauss关联结构图模型的充分降维方法. 中国科学：数学, 2018, 48: 1819–1830.

[11]. Ji J, He D, Feng Y, He Y, Xue F, Xie L. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data. Bioinformatics. 2017, 33(19):3080-3087.  R code available at https://github.com/jijiadong/JDINAC.

[12]. Ji J#, Yuan Z#, Zhang X, Xue F. A powerful score-based statistical test for group difference in weighted biological networks. BMC bioinformatics, 2016, 17: 86.

[13]. Yuan Z#, Ji J#, Zhang X, Xu J, Ma D, Xue F. A powerful weighted statistic for detecting group differences of directed biological networks. Scientific Reports, 2016, 6: 34159.

[14]. Yuan Z, Ji J, Zhang T, Liu Y, Zhang X, Chen W, Xue F. A novel chi-square statistic for detecting group differences between pathways in systems epidemiology. Statistics in Medicine, 2016, 35(29): 5512-5524.

[15]. Ji J, Yuan Z, Zhang X, Li F, Xu J, Liu Y, Li H, Wang J, Xue F. Detection for pathway effect contributing to disease in systems epidemiology with a case–control design. BMJ Open, 2015, 5(1): e006721.

代表性论文（*通讯作者，#共同第一作者）[其他论文见个人主页 https://faculty.sdu.edu.cn/jijiadong/en/zdylm/1339808/list/index.htm]

[1]. Chen H#, Guo Y, He Y, Ji J#, Liu L, Shi Y, Wang Y, Yu L, Zhang X. Simultaneous Differential Network Analysis and Classification for Matrix-variate Data with application to Brain Connectivity. Biostatistics. 2021. doi:10.1093/biostatistics/kxab007. R package "SDNCMV".

[2]. Ji J, He Y, Xie L, Liu L. Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model. Biometrics. 2020. doi.org/10.1111/biom.13359.  R package "MVDN" available at https://github.com/jijiadong/MVDN.

[3]. He Y, Chen H, Sun H, Ji J*, Shi Y*, Zhang X, Liu L. High-dimensional Integrative Copula Discriminant Analysis for Multi-omics Data. Statistics in Medicine. 2020, 39(30): 4869-4884.

[4]. Lin W#, Ji J#, Zhu Y, Li M, Zhao J, Xue F, Yuan Z. PMINR: pointwise mutual information-based network regression – with application to studies of lung cancer and Alzheimer’s disease. Frontiers in genetics. 2020, 11:556259.

[5]. Zhu Y#, Ji J#, Lin W, Li M, Liu L, Zhu H, Xue F, Li X, Zhou X, Yuan Z. MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease. BMC Genetics. 2020, 21:90.

[6]. He Y, Sun H, Ji J*, Zhang X. Robust Feature Screening for Multi-response Trans-elliptical Regression Model with Ultrahigh-dimensional Covariates. Random Matrices: Theory and Applications. 2020, 9(4), 2150001.

[7]. He Y, Zhang L, Ji J*, Zhang X. Robust feature screening for elliptical copula regression model. Journal of Multivariate Analysis. 2019, 173: 568-582.

[8]. Chen H, He Y, Ji J*, Shi Y*. A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction. Frontiers in Neurology. 2019, 10:1162.

[9]. He Y, Ji J*, Xie L, Zhang X, Xue F. A new insight into underlying disease mechanism through semi-parametric latent differential network model. BMC bioinformatics, 2018, 19(17): 493.

[10]. 何勇, 季加东*, 张新生.  Gauss关联结构图模型的充分降维方法. 中国科学：数学, 2018, 48: 1819–1830.

[11]. Ji J, He D, Feng Y, He Y, Xue F, Xie L. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data. Bioinformatics. 2017, 33(19):3080-3087.  R code available at https://github.com/jijiadong/JDINAC.

[12]. Ji J#, Yuan Z#, Zhang X, Xue F. A powerful score-based statistical test for group difference in weighted biological networks. BMC bioinformatics, 2016, 17: 86.

[13]. Yuan Z#, Ji J#, Zhang X, Xu J, Ma D, Xue F. A powerful weighted statistic for detecting group differences of directed biological networks. Scientific Reports, 2016, 6: 34159.

[14]. Yuan Z, Ji J, Zhang T, Liu Y, Zhang X, Chen W, Xue F. A novel chi-square statistic for detecting group differences between pathways in systems epidemiology. Statistics in Medicine, 2016, 35(29): 5512-5524.

[15]. Ji J, Yuan Z, Zhang X, Li F, Xu J, Liu Y, Li H, Wang J, Xue F. Detection for pathway effect contributing to disease in systems epidemiology with a case–control design. BMJ Open, 2015, 5(1): e006721.

代表性论文（*通讯作者，#共同第一作者）[其他论文见个人主页 https://faculty.sdu.edu.cn/jijiadong/en/zdylm/1339808/list/index.htm]

[1]. Chen H#, Guo Y, He Y, Ji J#, Liu L, Shi Y, Wang Y, Yu L, Zhang X. Simultaneous Differential Network Analysis and Classification for Matrix-variate Data with application to Brain Connectivity. Biostatistics. 2021. doi:10.1093/biostatistics/kxab007. R package "SDNCMV".

[2]. Ji J, He Y, Xie L, Liu L. Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model. Biometrics. 2020. doi.org/10.1111/biom.13359.  R package "MVDN" available at https://github.com/jijiadong/MVDN.

[3]. He Y, Chen H, Sun H, Ji J*, Shi Y*, Zhang X, Liu L. High-dimensional Integrative Copula Discriminant Analysis for Multi-omics Data. Statistics in Medicine. 2020, 39(30): 4869-4884.

[4]. Lin W#, Ji J#, Zhu Y, Li M, Zhao J, Xue F, Yuan Z. PMINR: pointwise mutual information-based network regression – with application to studies of lung cancer and Alzheimer’s disease. Frontiers in genetics. 2020, 11:556259.

[5]. Zhu Y#, Ji J#, Lin W, Li M, Liu L, Zhu H, Xue F, Li X, Zhou X, Yuan Z. MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease. BMC Genetics. 2020, 21:90.

[6]. He Y, Sun H, Ji J*, Zhang X. Robust Feature Screening for Multi-response Trans-elliptical Regression Model with Ultrahigh-dimensional Covariates. Random Matrices: Theory and Applications. 2020, 9(4), 2150001.

[7]. He Y, Zhang L, Ji J*, Zhang X. Robust feature screening for elliptical copula regression model. Journal of Multivariate Analysis. 2019, 173: 568-582.

[8]. Chen H, He Y, Ji J*, Shi Y*. A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction. Frontiers in Neurology. 2019, 10:1162.

[9]. He Y, Ji J*, Xie L, Zhang X, Xue F. A new insight into underlying disease mechanism through semi-parametric latent differential network model. BMC bioinformatics, 2018, 19(17): 493.

[10]. 何勇, 季加东*, 张新生.  Gauss关联结构图模型的充分降维方法. 中国科学：数学, 2018, 48: 1819–1830.

[11]. Ji J, He D, Feng Y, He Y, Xue F, Xie L. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data. Bioinformatics. 2017, 33(19):3080-3087.  R code available at https://github.com/jijiadong/JDINAC.

[12]. Ji J#, Yuan Z#, Zhang X, Xue F. A powerful score-based statistical test for group difference in weighted biological networks. BMC bioinformatics, 2016, 17: 86.

[13]. Yuan Z#, Ji J#, Zhang X, Xu J, Ma D, Xue F. A powerful weighted statistic for detecting group differences of directed biological networks. Scientific Reports, 2016, 6: 34159.

[14]. Yuan Z, Ji J, Zhang T, Liu Y, Zhang X, Chen W, Xue F. A novel chi-square statistic for detecting group differences between pathways in systems epidemiology. Statistics in Medicine, 2016, 35(29): 5512-5524.

[15]. Ji J, Yuan Z, Zhang X, Li F, Xu J, Liu Y, Li H, Wang J, Xue F. Detection for pathway effect contributing to disease in systems epidemiology with a case–control design. BMJ Open, 2015, 5(1): e006721.