A Markovian Model for Default Risk in a Network of Sectors. - Wai-Ki Ching; Ho-Yin Leung; Hao Jiang 009; Liang Sun (BIFE, 2009)

Modeling default risk via a hidden Markov model of multiple sequences. - Wai-Ki Ching; Ho-Yin Leung; Zhenyu Wu; Hao Jiang 009 (Frontiers Comput. Sci. China, 2010)

A Markovian network model for default risk management. - Wai-Ki Ching; Ho-Yin Leung; Hao Jiang 009; Liang Sun; Tak Kuen Siu (Int. J. Intell. Eng. Informatics, 2010)

The role of Eigen-matrix translation in classification of biological datasets. - Hao Jiang 009; Wai-Ki Ching (BIBM, 2012)

Metabolite biomarker discovery for metabolic diseases by flux analysis. - Limin Li; Hao Jiang 009; Wai-Ki Ching; Vassilios S. Vassiliadis (ISB, 2012)

On optimal control policy for probabilistic Boolean network: a state reduction approach. - Xi Chen; Hao Jiang 009; Yushan Qiu; Wai-Ki Ching (BMC Syst. Biol., 2012)

Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data. - Hao Jiang 009; Wai-Ki Ching (Comput. Math. Methods Medicine, 2012)

Discriminant analysis in pairwise kernel learning for SVM classification. - Hao Jiang 009; Wai-Ki Ching; Delin Chu (Int. J. Bioinform. Res. Appl., 2012)

Modeling genetic regulatory networks: a delay discrete dynamical model approach. - Hao Jiang 009; Wai-Ki Ching; Kiyoko F. Aoki-Kinoshita; Dianjing Guo (J. Syst. Sci. Complex., 2012)

Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach. - Limin Li; Hao Jiang 009; Yushan Qiu; Wai-Ki Ching; Vassilios S. Vassiliadis (BMC Syst. Biol., 2013)

On the Complexity of Inference and Completion of Boolean Networks from Given Singleton Attractors. - Hao Jiang 009; Takeyuki Tamura; Wai-Ki Ching; Tatsuya Akutsu (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2013)

Construction and computation methods for biological networks. - Hao Jiang 009 (2013)

On observability of attractors in Boolean Networks. - Yushan Qiu; Xiaoqing Cheng; Wai-Ki Ching; Hao Jiang 009; Tatsuya Akutsu (BIBM, 2015)

On Eigen-matrix translation method for classification of biological data. - Hao Jiang 009; Yushan Qiu; Xiaoqing Cheng; Wai-Ki Ching (J. Syst. Sci. Complex., 2015)

On using physico-chemical properties of amino acids in string kernels for protein classification via support vector machines. - Limin Li; Kiyoko F. Aoki-Kinoshita; Wai-Ki Ching; Hao Jiang 009 (J. Syst. Sci. Complex., 2015)

Unconstrained optimization in projection method for indefinite SVMs. - Hao Jiang 009; Wai-Ki Ching; Yushan Qiu; Xiaoqing Cheng (BIBM, 2016)

Hadamard Kernel SVM with applications for breast cancer outcome predictions. - Hao Jiang 009; Wai-Ki Ching; Wai-Shun Cheung; Wenpin Hou; Hong Yin (BMC Syst. Biol., 2017)

Optimal projection method determination by Logdet Divergence and perturbed von-Neumann Divergence. - Hao Jiang 009; Wai-Ki Ching; Yushan Qiu; Xiaoqing Cheng (BMC Syst. Biol., 2017)

Stationary Mahalanobis kernel SVM for credit risk evaluation. - Hao Jiang 009; Wai-Ki Ching; Ka Fai Cedric Yiu; Yushan Qiu (Appl. Soft Comput., 2018)

Discovery of Boolean metabolic networks: integer linear programming based approach. - Yushan Qiu; Hao Jiang 009; Wai-Ki Ching; Xiaoqing Cheng (BMC Syst. Biol., 2018)

Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection. - Yushan Qiu; Yulong Huang; Shaobo Tan; Dongqi Li; Ada Chaeli van der Zijp-Tan; Glen M. Borchert; Hao Jiang 009; Jingshan Huang (IEEE Access, 2019)

On predicting epithelial mesenchymal transition by integrating RNA-binding proteins and correlation data via L1/2-regularization method. - Yushan Qiu; Hao Jiang 009; Wai-Ki Ching; Michael K. Ng 001 (Artif. Intell. Medicine, 2019)

Drug Side-Effect Profiles Prediction: From Empirical to Structural Risk Minimization. - Hao Jiang 009; Yushan Qiu; Wenpin Hou; Xiaoqing Cheng; Man Yi Yim; Wai-Ki Ching (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)

Informative gene identification for Single-Cell RNA-Seq Data with Mutual Information based Firefly Algorithm. - Yiyao Dou; Hao Jiang 009 (ICBBE, 2021)

HOMC: A Hierarchical Clustering Algorithm Based on Optimal Low Rank Matrix Completion for Single Cell Analysis. - Xiaoqing Cheng; Chang Yan; Hao Jiang 009; Yushan Qiu (ICIC (3), 2021)

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions. - Yushan Qiu; Hao Jiang 009; Wai-Ki Ching (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Eogface: Deep Face Recognition via Extensional Logits. - Xingying Zhao; Hao Jiang 009; Dong Shen (ICIP, 2022)

A high-order norm-product regularized multiple kernel learning framework for kernel optimization. - Hao Jiang 009; Dong Shen; Wai-Ki Ching; Yushan Qiu (Inf. Sci., 2022)

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy. - Xiaoqing Cheng; Chang Yan; Hao Jiang 009; Yushan Qiu (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

A Novel Adaptive Gain Strategy for Stochastic Learning Control. - Xiang Cheng; Hao Jiang 009; Dong Shen; Xinghuo Yu 001 (IEEE Trans. Cybern., 2023)

Decentralized learning control for large-scale systems with gain-adaptation mechanisms. - Hao Jiang 009; Xun He; Qijiang Song; Dong Shen (Inf. Sci., 2023)

Optimal Learning Control Scheme for Discrete-Time Systems With Nonuniform Trials. - Chen Liu; Xiaoe Ruan; Dong Shen; Hao Jiang 009 (IEEE Trans. Cybern., 2023)

scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data. - Yixiang Huang; Hao Jiang 009; Wai-Ki Ching (Briefings Bioinform., 2024)

Data Valuation by Leveraging Global and Local Statistical Information. - Xiaoling Zhou; Ou Wu; Michael K. Ng 001; Hao Jiang 009 (CoRR, 2024)

History Makes the Future: Iterative Learning Control for High-Speed Trains. - Shuai Gao; Qijiang Song; Hao Jiang 009; Dong Shen (IEEE Intell. Transp. Syst. Mag., 2024)

AGML: Adaptive Graph-Based Multi-Label Learning for Prediction of RBP and as Event Associations During EMT. - Yushan Qiu; Wensheng Chen; Wai-Ki Ching; Hongmin Cai; Hao Jiang 009; Quan Zou 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2024)

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems. - Zeyi Zhang; Hao Jiang 009; Dong Shen; Samer S. Saab 001 (IEEE CAA J. Autom. Sinica, 2024)

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method. - Hao Jiang 009; Jingxin Liu; You Song; Jinzhi Lei (J. Comput. Biol., 2024)

Accelerated Learning Control for Point-to-Point Tracking Systems. - Hao Jiang 009; Dong Shen; Shunhao Huang; Xinghuo Yu 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

A Novel Accelerated Multistage Learning Control Mechanism via Virtual Performance Reduction. - Xiang Cheng; Hao Jiang 009; Dong Shen (IEEE Trans. Neural Networks Learn. Syst., 2024)

An Accelerated Adaptive Gain Design in Stochastic Learning Control. - Xiang Cheng; Hao Jiang 009; Dong Shen; Xinghuo Yu 001 (IEEE Trans. Cybern., 2024)

scRECL: representative ensembles with contrastive learning for scRNA-seq data clustering analysis. - Yixiang Huang; Hao Jiang 009; Wai-Ki Ching; Dong Shen (Briefings Bioinform., 2025)

Co-regularized optimal high-order graph embedding for multi-view clustering. - Senwen Zhan; Hao Jiang 009; Dong Shen (Pattern Recognit., 2025)

Noisy Error-Adaptive Weighting Strategy for Accelerating ILC in Discrete-Time Systems. - Zeyi Zhang; Dong Shen; Hao Jiang 009; Samer Saab 001; Xinghuo Yu 001 (IEEE Trans. Cybern., 2025)