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On Eigen-Matrix Translation Method for Classification of Biological Data

Journal of Systems Science & Complexity 2015年第5期28卷 1212-1230页

作者： JIANG Hao QIU Yushan CHENG Xiaoqing CHING Waiki Department of Mathematics School of Information Renmin University of China Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong

Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines(SVMs)are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigenmatrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.

关键词： Classification dimension reduction eigen-matrix translation glycan data kernel method(KM) support vector machine(SVM)

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modeling GENETIC REGULATORY NETWORKS:A DELAY DISCRETE DYNAMICAL MODEL APPROACH

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Journal of Systems Science & Complexity 2012年第6期25卷 1052-1067页

作者： Hao JIANG Wai-Ki CHING Kiyoko F.AOKI-KINOSHITA Dianjing GUO Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Bioinformatics Faculty of EngineeringSoka University Department of Biology The Chinese University of Hong Kong

modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.

关键词： Delay effect discrete dynamic system model genetic regulatory networks k-means clustering method linear multiple regression.

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A Markov-Driven Portfolio Execution Strategy with Market Impact

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高等学校计算数学学报（英文版） 2018年第4期11卷 701-728页

作者： Qingqing Yang Wai-Ki Ching Tak-Kuen Siu Zhiwen Zhang Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Pokfulam Road Hong Kong Department of Applied Finance and Actuarial Studies Faculty of Business and Economics Macquarie University Sydney NSW 2109 Australia

In this paper,we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market *** Almgren-Chriss's market impact model [1] is extended to a more general situation where multiple venues are available for investors to submit *** the assumption of risk-neutrality,a compact recursive formula is derived,using the value iterative method,to calculate the optimal agency execution *** original optimal control problem is then converted to a constrained quadratic optimization problem,which can be solved by using the Quadratic Programming (Qp) *** examples are given to illustrate the efficiency and effective of our proposed methods.

关键词： Hamilton-Jacobi-Bellman (HJB) equation Limit Order Book (LOB) Dynamic Programming (DP) principle market impact Quadratic Programming (Qp) regime-switching value iteration method

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The role of Eigen-matrix translation in classification of biological datasets

The role of Eigen-matrix translation in classification of bi...

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2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012

作者： Jiang, Hao Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781467325585

Driven by the challenge of integrating large amount of experimental data obtained from biological research, computational biology and bioinformatics are growing rapidly. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular tools. In the perspective of kernel matrix, a technique namely Eigen-matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy owns a lot of nice properties while the nature of which needs further exploration. We propose that its importance lies in the dimension reduction of predictor attributes within the data set. This can therefore serve as a novel perspective for future research in dimension reduction problems. © 2012 IEEE.

关键词： Classification (of information)

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A semi-tensor product approach for Probabilistic Boolean Networks 8

A semi-tensor product approach for Probabilistic Boolean Net...

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8th International Conference on Systems Biology, ISB 2014

作者： Cheng, Xiaoqing Qiu, Yushan Hou, Wenpin Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781479972944

modeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach. © 2014 IEEE.

关键词： Matrix algebra

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On classification of biological data using outlier detection 12

On classification of biological data using outlier detection

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12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management, ISORA 2015

作者： Qiu, Yushan Cheng, Xiaoqing Hou, Wenpin Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong

ISBN: (纸本)9781785610851

With the rapid development of information technology, the number of datasets, as well as their complexity and dimension, have been growing dramatically. This dramatic growth of biology data and non-biological commercial databases becomes a challenging issue in data mining. Classification technique is one of the major tools in the captured research area. However, the performance of classification may be degraded when there exists noise in the captured databases. Therefore, outlier detection becomes an urgent need and the issue of how to integrate outlier detection method and classification techniques is an important and challenging issue. In this paper, we proposed a novel and effective approach based on k-means clustering to identify outliers in the databases. In particular, we employed one of famous classification techniques, Support Vector Machine (SVM), owing to its ability to handle highdimensional data set. We also compare the classification results with the multivariate outlier detection method. Numerical results on two different data sets indicate that the classification results after removing the outliers by our proposed method are much better than the multivariate outlier detection method. © 2015 IET.

关键词： Classification (of information)

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On Using Physico-Chemical Properties of Amino Acids in String Kernels for Protein Classification via Support Vector Machines

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Journal of Systems Science & Complexity 2015年第2期28卷 504-516页

作者： LI Limin AOKI-KINOSHITA Kiyoko F CHING Wai-Ki JIANG Hao Institute of Information and System Science Xi'an Jiaotong University Department of Bioinformatics Faculty of EngineeringSoka University Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Mathematics School of InformationRenmin University of China

String kernels are popular tools for analyzing protein sequence data and they have been successfully applied to many computational biology problems. The traditional string kernels assume that different substrings are independent. However, substrings can be highly correlated due to their substructure relationship or common physico-chemical properties. This paper proposes two kinds of weighted spectrum kernels： The correlation spectrum kernel and the AA spectrum kernel. We evMuate their performances by predicting glycan-binding proteins of 12 glycans. The results show that the correlation spectrum kernel and the AA spectrum kernel perform significantly better than the spectrum kernel for nearly all the 12 glycans. By comparing the predictive power of AA spectrum kernels constructed by different physico-chemical properties, the authors can also identify the physico- chemical properties which contributes the most to the glycan-protein binding. The results indicate that physico-chemical properties of amino acids in proteins play an important role in the mechanism of glycamprotein binding.

关键词： AAindex AA spectrum kernel correlation spectrum kernel physico-chemical properties string kernel weighted spectrum kernel.

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A new estimation method for multivariate Markov chain model with application in demand predictions

A new estimation method for multivariate Markov chain model ...

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International Conference on Business Intelligence and Financial Engineering

作者： Zhu, Dong-Mei Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

ISBN: (纸本)9780769541167

In this paper, we propose a new estimation method for the parameters of a multivariate Markov chain model. In the new method, we calculate the correlations of the sequences first and establish multivariate Markov chain models for those positively correlated sequences. The parameters are estimated by minimizing the error of prediction. We apply the method to demand predictions for a soft-drink company in Hong Kong. Numerical experiments are given to show the effectiveness of our proposed method. © 2010 IEEE.

关键词： Forecasting

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Quantity discount contract for supply chain coordination with false failure returns

Quantity discount contract for supply chain coordination wit...

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International Conference on Natural Computation

作者： Huang, Ximin Choi, Sin-Man Ching, Wai-Ki Department of Mathematics Advanced Modeling and Applied Computing Laboratory University of Hong Kong Pokfulam Road Hong Kong Hong Kong

ISBN: (纸本)9781424459612

Consumer return attracts more and more academic attention due to its rapidly expanding size, and a large portion of it falls into the category of false failure return, which refers to return without functional defect. In this paper, we exclusively consider profit results from exerting costly effort to reduce false failure returns in a reverse supply chain consisting of a retailer and a supplier. The supply chain as a whole has strong incentive to reduce false failure returns because it can avoid much reprocessing cost associated. But typically, retailers enjoy a full credit provided by suppliers in case of returns, and hence they may not have sufficient incentives to exert enough effort for supply chain profit maximization. In some scenarios they may even have the motivation to actually encourage such returns. We suggest using a coordination contract to resolve such profit conflicts. The contract we propose is a quantity discount contract specifying a payment to the retailer with an amount exponentially decreasing in the number of false failure returns. We give explicit forms of such contracts given different assumptions about distribution of the number of returns and we also prove that such contract is capable of increasing both retailer's and supplier's profit simultaneously. Besides, when the contract is used together with other forward supply chain coordination contracts in a closed-loop chain, it is shown that it can act to deter retailer's potential incentive to encourage false failure returns. Moreover, some modifications of the contract may lead to easy allocation of incremental profit within the supply chain. © 2010 IEEE.

关键词： Supply chains

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On improving incentive in a supply chain: Wholesale price contract vs quantity dependent contract

On improving incentive in a supply chain: Wholesale price co...

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40th International Conference on Computers and Industrial Engineering, CIE40 2010

作者： Huang, Ximin Choi, Sin-Man Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

ISBN: (纸本)9781424472956

In this paper, we first study the performance of a supply chain consisting of one retailer and one supplier. The supplier sets the price scheme of some goods and the retailer then decides the order level and sells the goods in the market. Specifically, a quadratic cost function is assumed here to approximate the U-shape cost curve commonly observed in industries. Two kinds of contracts offered by the supplier are investigated, namely wholesale price contract and quantity dependent contract. Wholesale price is fixed under the first contract but varies depending on order level under the second one. We show that certain wholesale price contract successfully induces the retailer to order at a level such that supply chain profit is maximized, but extra cost in implementation may occur due to supplier's disagreement on this price. Given this, we propose an efficiency measure to show to what extent the wholesale price contract helps to increase supply chain profit. For quantity dependent contract, we show that it can coordinate the supply chain and leads to a proportional division of supply chain profit. We then generalize the analysis to cover the case of multiple retailers and single supplier where similar results are also obtained.

关键词： Game theory

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