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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

引用

IEEE International Conference on Systems Biology

作者： Mingjun Wang Hong-Bin Shen Tatsuya Akutsu Jiangning Song State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences (CAS) Tianjin China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC Australia

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations.

关键词： Support vector machines Accuracy Proteins Feature extraction Humans Amino acids Databases

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

引用

2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424481354

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved. © 2010 IEEE.

关键词： Support vector machines

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Jiang, Ziheng He, Jing Guo, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781424481354

In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique. © 2010 IEEE.

关键词： Support vector machines

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An accurately located method and secure scheme for image authentication

An accurately located method and secure scheme for image aut...

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International Conference on Multimedia Technology

作者： Wang, Zuxi Wang, Wenzong Dong, Liefeng Ye, Peng Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China Ningbo Polytechnic Ningbo China

ISBN: (纸本)9781424478743

A novel authentication watermarking scheme for images is proposed in this paper, which holds accuracy location and high security at the same time. In the scheme, different keys are selected for different host data, and the generation and insertion of watermark is dependent on the selected key. Analyzing different pieces of watermarked data in order to launch an attack becomes impossible because of the lack of common characteristics in these pieces. Beside, to increase the security of system, the chaos technology is used to encrypt the watermark and shuffle the location of embedding. A method for tamper detecting based on the running window is proposed in the scheme. Not only can it distinguish malicious attacks from unintentional attacks, but also it can locate the tampered area accurately. Experimental results show this semi-fragile watermarking algorithm is effective and practical for content authentication. ©2010 IEEE.

关键词： Authentication

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Unified restoration method for different degraded images

Unified restoration method for different degraded images

引用

2010 International Conference on Optoelectronics and image processing, ICOIP 2010

作者： Hong, Hanyu Li, Liangcheng Yan, Luxin Zhang, Tianxu Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan 430074 China Institute for Pattern Recognition and AI Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769542522

Blurred images are caused by many factors such as defocus, motion, and atmospheric turbulence. Due to the unknown various factors that cannot be distinguished in the blurred image, it is necessary to propose a unified method for image restoration. In order to solve this problem, a unified restoration method for different degraded images is presented, in which we use edge information of blurred image to predict the blur mode. It operates by locally predicting a sharp image using edge information of blurred image and establishes a nonlinear and spatial anisotropic regularization function from the prediction sharp image and the blurred image. Then we can estimate the point spread function (PSF) and recover the blurred image quickly. A series of restoration experiments for different blured images have been performed to verify the effectiveness of the proposed method. © 2010 IEEE.

关键词： Restoration

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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

引用

International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ziheng Jiang Jing He Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

关键词： Feature extraction Training Semantics Support vector machine classification Testing Neurons

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China The School of Computer Science and Technology Beijing Institute of Technology Beijing China

关键词： Feature extraction image color analysis Support vector machines Discrete cosine transforms Histograms Semantics Kernel

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Modeling multi-source remote sensing image classifier based on the MDL principle: Experimental studies

Modeling multi-source remote sensing image classifier based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huaiying Xia Rukun Hu Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

引用

International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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