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A directional algorithm based on shape context for 3D point pattern matching

Journal of computational Information Systems 2015年第7期11卷 2355-2365页

作者： Pang, Kuo Liang, Yanchun Zhou, You Wang, Kangping Wang, Shu Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Department of Computer Science and Technology Zhuhai College of Jilin University Zhuhai China

Point pattern matching is the basis of image recognition and computer vision. Point pattern matching in three dimensional space with the presence of noise and outlier is an important research focus. In this paper, we present a novel method for point pattern matching base on shape context. Particularly, we aim to find robust feature points to determine the positive direction of the point set for three dimensional rotation. At the same time, we improve the feature extraction and similarity measurement, and then combine with bipartite map matching. Comparative experiments on both synthetic point sets and real data sets demonstrate that the proposed algorithm is effective and robust. ©, 2015, Binary Information Press. All right reserved.

关键词： Image recognition

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Improved filter feature selection methods by samples localization

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Journal of Bionanoscience 2013年第4期7卷 371-377页

作者： Sun, Ying Wang, Yan Chen, Liang Du, Wei Cao, Zhongbo Zhou, You College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Chemistry Jilin University Changchun 130012 China

With the development of high-throughput microarray technology, a large number of microarray data has been obtained by tens of thousands of simulation experiments on gene expression. However, due to the high cost, gene expression data of each experiment has a small number of samples for tens of thousands of genes. Furthermore, most of the genes, such as the features, are unrelated to classification. Therefore, developing an effective and robust method to extract informative genes subset from high dimensional microarray data is a challenging and important issue for microarray data analysis. In this article, we propose an improved feature selection method by samples localization. We only use localized samples to select genes subset, in order to effectively overcome the influence on the results of feature selection by outlier samples and the distribution of samples. Experimental results obtained by SVM classifiers on Leukemia dataset, Prostate dataset, Colon dataset, Breast dataset, DLBCL dataset and Lung dataset demonstrate that the proposed method is superior to those original filter feature selection methods both in best and average accuracies. Copyright © 2013 American Scientific Publishers. Website © 2013 Publishing Technology.

关键词： Feature Selection

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Making Simple Tabular Reduction works on negative table constraints

Making Simple Tabular Reduction works on negative table cons...

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27th AAAI Conference on Artificial Intelligence, AAAI 2013

作者： Li, Hongbo Liang, Yanchun Guo, Jinsong Li, Zhanshan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)9781577356158

Simple Tabular Reduction algorithms (STR) work well to establish Generalized Arc Consistency (GAC) on positive table constraints. However, the existing STR algorithms are useless for negative table constraints. In this work, we propose a novel STR algorithm and its improvement, which work on negative table constraints. Our preliminary experiments are performed on some random instances and a certain benchmark instances. The results show that the new algorithms outperform GAC-valid and the MDD-based GAC algorithm. Copyright © 2013, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Benchmarking

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An hidden markov model-based transcription factor mining method

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Journal of Bionanoscience 2013年第1期7卷 66-71页

作者： Wu, Chunguo Li, Gaoyang Wu, Xiaozhou Han, Xiaosong Wang, Binghong Breis, Jesualdo Tomás Fernández Wang, Fei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China Business School University of Shanghai for Science and Technology Shanghai 200093 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433 China Department of Informatics and Systems University of Murcia 30100 Murcia Spain

A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is defined to filter sentences. Transcription factor names are mined according to the part of speech tagged by hidden Markov model. Experimental results show that the recall and precision of HMM-TFM achieve 74.2% and 77.9%, respectively. Copyright © 2013 American Scientific Publishers.

关键词： Data mining

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Central Author Mining from Co-authorship Network

Central Author Mining from Co-authorship Network

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International Symposium on computational Intelligence and Design, ISCID

作者： Tao Peng Delong Zhang Xiaoming Liu Shang Wang Wanli Zuo Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.

关键词： Communities Collaboration Social network services Corporate acquisitions Clustering algorithms Correlation Network topology

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Effective and stable feature selection method based on filter for gene signature identification in paired microarray data

Effective and stable feature selection method based on filte...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhongbo Cao Yan Wang Ying Sun Wei Du Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect of paired samples. In this article, we propose a new feature selection method for paired microarray datasets based on the original paired t-test approach. We apply on the paired datasets across six common cancer types. Through comparison with some widely used methods on the performance of prediction power, stability of gene lists and functional stability, our method shows excellent performance. The proposed method has good effectiveness, stability and consistency, which enables the method to be applicative to feature selection for paired microarray expression data analysis.

关键词： Stability analysis Cancer Accuracy Signal to noise ratio Gene expression Redundancy Liver

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A Sarsa() algorithm based on double-layer fuzzy reasoning

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Mathematical Problems in Engineering 2013年 2013卷

作者： Liu, Quan Mu, Xiang Huang, Wei Fu, Qiming Zhang, Yonggang School of Computer Science and Technology Soochow University Suzhou Jiangsu 215006 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classic Q-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa() based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions;the second reasoning layer uses fuzzy sets of action to compute the components of Q-value. Then, these two fuzzy layers are combined to compute the Q-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa() to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance. © 2013 Quan Liu et al.

关键词： Fuzzy sets

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Fold-based meta-analysis: a method for identifying differentially expressed genes in microarray data

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Journal of Information and computational Science 2013年第11期10卷 3453-3462页

作者： Tian, Yuan Bai, Tian Liu, Guixia Li, Zhangxiu Wu, Jianan Zhou, Chunguang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Changchun University Changchun 130022 China

The increase of microarray experiments brings a fresh challenge to analyze microarray data across datasets. Several methods have been developed and implemented. But the current tools were either complicated software with inefficiency methods or web applications, among which some were limited to specific data format. In this paper, we propose Fold-based Meta-analysis (FM), a method to identify Differentially Expressed Genes (DEGs) in microarray data. It could meta-analyze datasets in either the same or different platforms. We test FMT, respectively, on two published Arabidopsis datasets in one platform and three Mouse datasets in three different platforms, and then analyze the Gene Ontology (GO) terms or metabolic pathways of the most DEGs. Our results show that they are highly agreed on DEGs, and many of them are crucial and functional. In summary, our method is fast and practical. © 2013 Binary Information Press.

关键词： Genes

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Prediction of multiple sequence alignment based on an improved particle swarm optimization

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Journal of Bionanoscience 2013年第1期7卷 92-96页

作者： Gao, Yu-Xi Wang, Yan-Min Pei, Zhi-Li Department of Computer Science Jilin Business and Technology College China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China College of Computer Science and Technology Inner Mongolia University for the Nationalities Tongliao 028000 China

In this paper we use an improved Particle Swarm Optimization algorithm to solve Multiple Sequence Alignment (MSA). MSA is a key problem in bioinformatics. The thesis starts with the theory of Particle swarm optimization algorithm and the design an improved algorithm for Multiple Sequence Alignment. The simulated experiments show that the improved algorithm surpasses existing algorithms in performance. Copyright © 2013 American Scientific Publishers.

关键词： Bioinformatics

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Prediction of ncRNA based on least squares support vector machine

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Journal of Bionanoscience 2013年第1期7卷 121-125页

作者： Xing, Chong Wan, Liming Wang, Jiaxin Liang, Yanchun College of Electrical and Information Engineering Guanghua College of Changchun University Changchun 130033 China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

In this paper, on the basis of principal component analysis, we use least squares support vector machine (LS-SVM) to predict tRNA. Appearance frequencies of single nucleotide, 2-nucleotides, (G-C)% and (A-T)% were chosen as characteristic inputs. Results from tests showed that the prediction accuracy was 90.51% on prokaryotic tRNA dataset. The results indicate that the proposed method is adoptable for prokaryotic ncRNA prediction. Copyright © 2013 American Scientific Publishers.

关键词： RNA

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