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A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy

Journal of Bionic engineering 2008年第3期5卷 215-223页

作者： Shu-xue Zou Yan-xin Huang Yan Wang Chun-guang Zhou Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 P. R. China

Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.

关键词： protein domain boundary SVM imbalanced data learning distance-based maximal entropy

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Canonical Huffman code based full-text index

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Progress in Natural Science:Materials International 2008年第3期18卷 325-330页

作者： Yi Zhang a,b, Zhili Pei a, Jinhui Yanga, Yanchun Liang a, a College of Computer Science and Technology, Jilin University, key laboratory of symbol computation and knowledge engineering of the Ministry of Education, Changchun 130012, China b Department of Computer Science, Jilin Technology and Business College, Changchun 130062, China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Department of Computer Science Jilin Technology and Business College Changchun 130062 China

Full-text indices are data structures that can be used to find any substring of a given string. Many full-text indices require space larger than the original string. In this paper, we introduce the canonical Huffman code to the wavelet tree of a string T[1. . .n]. Compared with Huffman code based wavelet tree, the memory space used to represent the shape of wavelet tree is not needed. In case of large alphabet, this part of memory is not negligible. The operations of wavelet tree are also simpler and more efficient due to the canonical Huffman code. Based on the resulting structure, the multi-key rank and select functions can be performed using at most nH0 + jRj(lglgn + lgn lgjRj)+O(nH0) bits and in O(H0) time for average cases, where H0 is the zeroth order empirical entropy of T. In the end, we present an efficient construction algorithm for this index, which is on-line and linear.

关键词： Full-text index Su?x automaton Canonical Hu?man code

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PSO based partitional clustering algorithm

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第6期38卷 1371-1377页

作者： Zhang, Chang-Sheng Sun, Ji-Gui Cui, Yan Yang, Feng-Qin Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.

关键词： Clustering algorithms

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Corrigendum to “Catalog segmentation with double constraints in business” [Pattern Recognition Letters 30 (4) (2009) 440–448]

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Pattern Recognition Letters 2009年第15期30卷 1440-1440页

作者： Xiujuan Xu Yu Liu Zhe Wang Chunguang Zhou Yanchun Liang College of Computer Science Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China School of Software Dalian University of Technology Dalian Economic and Technological Development Zone Liaoning Number 8 Road Dalian 116620 China

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Efficient collision detection algorithm based on separating bounding volumes

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Ruan Jian Xue Bao/Journal of Software 2008年第SUPPL.期19卷 143-150页

作者： Wang, Yi Li, Wen-Hui Zhang, Zhen-Hua Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

An efficient collision detection method based on separating bounding volume (SBV) is proposed. The positions and shapes of SBVs are determined by the optimal separating support hyper planes of two objects. SBVs not only can efficiently detect the separation of models, but have a high culling ratio when models are intersecting. In order to compute SBVs efficiently, an approximate method using SVM is also put forward and tested. At last in penetration region, a method combined with GPU and SBVs is designed to handle the proximity queries. Experimental results illustrate that SBVs based collision detection algorithm is applicable to exact collision detection for 3D models even without topologies and achieves more efficient and balanced performances in separating, colliding and especially puncturing cases. © 2008 by Journal of Software. All rights reserved.

关键词： Support vector machines

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Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

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Journal of Bionic engineering 2008年第2期5卷 111-119页

作者： Jin-hui Yang Liang Sun Heow Pueh Lee Yun Qian Yan-chun Liang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 P. R. China Institute of High Performance Computing Singapore 117528 Singapore Department of Mechanical Engineering National University of Singapore Singapore 117576 Singapore Institute of Electric and Information Engineering Beihua University Jilin 13202 l P. R. China

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.

关键词： job shop scheduling problem clonal selection algorithm simulated annealing global search local search

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Self-organization based divide and conquer algorithm for distributed constraint optimization problems

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第11期45卷 1831-1839页

作者： Huang, Jing Liu, Dayou Yang, Bo Jin, Di College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

Distributed constraint optimization problem (DCOP) is a kind of optimization problem oriented to large-scale, open and dynamic network environments, which has been widely applied in many fields such as computational grid, multimedia networks, e-business, enterprise resource planning and so on. Besides the features such as non-linear and constraint-satisfaction which the traditional optimization problems have, DCOP has its distinct features including dynamic evolution, regional information, localized control and asynchronous updating of network states. It has become a challenge for computer scientists to find out a large-scale, parallel and intelligent solution for DCOP. So far, there have been a lot of methods for solving this problem. However, most of them are not completely decentralized and require prior knowledge such as the global structures of networks as their inputs. Unfortunately, for many applications the assumption that the global views of networks can not be obtained beforehand is not true due to their huge sizes, geographical distributions or decentralized controls. To solve this problem, a self-organizing behavior based divide and conquer algorithm is presented, in which multiple autonomous agents distributed in networks work together to solve the DCOP through a proposed self-organization mechanism. Compared with existing methods, this algorithm is completely decentralized and demonstrates good performance in both efficiency and effectiveness.

关键词： Geographical distribution

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Logical reinforcement learning method based on heuristic contour list

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第11期45卷 1824-1830页

作者： Liu, Quan Gao, Yang Chen, Daoxu Sun, Jigui Yao, Wangshu State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Institute of Computer Science and Technology Soochow University Suzhou 215006 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

Reinforcement learning gets optimal policy through trial-and-error and interaction with dynamic environment. Its properties of self-improving and online learning make reinforcement learning become one of most important machine learning methods. Against reinforcement learning has been 'curse of dimensionality' troubled by the problem the question, a method of heuristic contour list is proposed on the basis of relational reinforcement learning. The method can represent states, actions and Q-functions through using first-order predications with contour list. Thus advantages of Prolog list can be exerted adequately. The method is to combine logical predication rule with reinforcement learning. A new logical reinforcement learning-CCLORRL is formed and its convergence is proved. The method uses contour shape predicates to build shape state tables, drastically reducing the state space;Using heuristic rules to guide the choice of action can reduce choice blindness when the sample does not exist in the state space. The CCLORRL algorithm is used in the Tetris game. Experiments show that the method is more efficient.

关键词： Reinforcement learning

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The Application of Support Vector Machine to Operon Prediction

The Application of Support Vector Machine to Operon Predicti...

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International Conference on Future Generation Communication and Networking (FGCN)

作者： Xiumei Wang Wei Du Yan Wang Chen Zhang Chunguang Zhou Shuqin Wang Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781424440085

In this paper, we apply the least-square support vector machine (LS-SVM) to operon prediction of Escherichia coli (***), with different combinations of intergenic distance, gene expression data, and phylogenetic profile. Experimental results demonstrate that the WO pairs tend to have shorter intergenic distances, higher correlation coefficient and much stronger relation of co-envoled between phylogenetic profiles. Also, we dealt with the data sets extracted from WOs¿ and TUBs¿, processed the intergenic distances with log-energy entropy, de-noised the Pearson correlation coefficients of two genes expression data with wavelet transform, and computed the Hamming distances of two phylogenetic profiles. Then we trained LS-SVM using part of the data sets and tested the trained classifier model using the rest data sets. It shows that different combinations of features could affect the prediction results. When the combination of intergenic distance, gene expression data and phylogenetic profile is taken as the input of LS-SVM in the linear kernel type, good results can be obtained, of which the accuracy, sensitivity and specificity are 92.34%, 93.54%, and 90.73%, respectively.

关键词： Support vector machines Phylogeny Gene expression Data mining Entropy Genetic expression Wavelet transforms Testing Kernel Sensitivity and specificity

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Copyright Protection Using A computational Watermarking Scheme

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The 9th International Conference for Young Computer Scientists(第九届国际青年计算机大会)

作者： Qi Jiang Dongfeng Han Yi Li Wenhui Li Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

In this paper an efficient copyright protection watermarking algorithm is proposed. By embedding orthogonal vector into the wavelet-tree structure of the host image, we get the watermarked image. At the same time human vision system is considered to get perceptual results. We design an elaborate function for blind watermarking scheme. This function can dynamically determine the embedding position. The theory and experimental results show that our method successfully survives image processing operation, noise adding, the JPEG lossy compression and image cropping. Especially, the scheme is robust towards image sharpening and image enhancement.

关键词： copyright protection watermark wavelet tree

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