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5th international and Interdisciplinary conference CONTEXT 2005 - Modeling and Using Context

作者： Nagy, Naomi Zhang, Xiaoli Nagy, George Schneider, Edgar W. English Department University of New Hampshire Durham NH 03824 United States DocLab. ECSE Rensselaer Polytechnic Institute Troy NY 12180 United States Department of English Linguistics Regensburg University Regensburg Germany

ISBN: (纸本)354026924X

We test a method of clustering dialects of English according to patterns of shared phonological features. Previous linguistic research has generally considered phonological features as independent of each other, but context is important: rather than considering each phonological feature individually, we compare the patterns of shared features, or Mutual Information (MI). the dependence of one phonological feature on the others is quantified and exploited. the results of this method of categorizing 59 dialect varieties by 168 binary internal (pronunciation) features are compared to traditional groupings based on external features (e.g., ethnic, geographic). the MI and size of the groups are calculated for taxonomies at various levels of granularity and these groups are compared to other analyses of geographic and ethnic distribution. Applications that could be improved by using MI methods are suggested. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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Augmented cell-graphs for automated cancer diagnosis

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bioinformatics 2005年第Sup2期21卷 7-12页

作者： Demir, C Gultekin, SH Yener, B Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA CUNY Mt Sinai Sch Med Dept Pathol New York NY 10029 USA

this work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.

关键词： Algorithms Artificial Intelligence Brain Neoplasms/pathology Glioma/pathology Humans Image Interpretation, Computer-Assisted/methods pattern recognition, Automated/methods Reproducibility of Results Sensitivity and Specificity

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Classifying noisy protein sequence data: a case study of immunoglobulin light chains

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bioinformatics 2005年第Sup1期21卷 I495-I501页

作者： Yu, CG Zavaljevski, N Stevens, FJ Yackovich, K Reifman, J Argonne Natl Lab Argonne IL 60439 USA Clarion Univ Pennsylvania Dept Comp Informat Sci Clarion PA 16214 USA USA Med Res & Mat Command Ft Detrick MD 21702 USA

the classification of protein sequences obtained from patients with various immunoglobulin-related conformational diseases may provide insight into structural correlates of pathogenicity. However, clinical data are very sparse and, in the case of antibody-related proteins, the collected sequences have large variability with only a small subset of variations relevant to the protein pathogenicity ( function). On this basis, these sequences represent a model system for development of strategies to recognize the small subset of function-determining variations among the much larger number of primary structure diversifications introduced during evolution. Under such conditions, most protein classification algorithms have limited accuracy. To address this problem, we propose a support vector machine (SVM)-based classifier that combines sequence and 3D structural averaging information. Each amino acid in the sequence is represented by a set of six physicochemical properties: hydrophobicity, hydrophilicity, volume, surface area, bulkiness and refractivity. Each position in the sequence is described by the properties of the amino acid at that position and the properties of its neighbors in 3D space or in the sequence. A structure template is selected to determine neighbors in 3D space and a window size is used to determine the neighbors in the sequence. the test data consist of 209 proteins of human antibody immunoglobulin light chains, each represented by aligned sequences of 120 amino acids. the methodology is applied to the classification of protein sequences collected from patients with and without amyloidosis, and indicates that the proposed modified classifiers are more robust to sequence variability than standard SVM classifiers, improving classification error between 5 and 25% and sensitivity between 9 and 17%. the classification results might also suggest possible mechanisms for the propensity of immunoglobulin light chains to amyloid formation.

关键词： Algorithms Amino Acids/chemistry Computational Biology/methods Databases, Protein Databases, Protein Humans Imaging, three-Dimensional Immunoglobulin Light Chains/chemistry Models, Statistical pattern recognition, Automated Protein Conformation Proteins/chemistry ROC Curve Sequence Analysis, Protein/methods

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A fully Bayesian model to cluster gene-expression profiles

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bioinformatics 2005年第Sup2期21卷 130-136页

作者： Vogl, C Sanchez-Cabo, F Stocker, G Hubbard, S Wolkenhauer, O Trajanoski, Z Vet Med Univ Vienna Inst Genet & Anim Breeding A-1210 Vienna Austria Graz Univ Technol Inst Genom & Bioinformat A-8010 Graz Austria Graz Univ Technol Christian Doppler Lab Genom & Bioinformat A-8010 Graz Austria Univ Manchester Fac Life Sci Manchester M60 1QD Lancs England Univ Rostock Inst Informat D-18051 Rostock Germany

Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance;results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data. Results: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. the number of classes does not need to be specified in advance;instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing. Availability: the code is available at http://***/BayesianClustering/.

关键词： Algorithms Artificial Intelligence Bayes theorem Cluster Analysis Computer Simulation Gene Expression Profiling/methods Models, Genetic Multigene Family/physiology Oligonucleotide Array Sequence Analysis/methods pattern recognition, Automated/methods

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Growing Bayesian network models of gene networks from seed genes

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bioinformatics 2005年第Sup2期21卷 224-229页

作者： Peña, JM Björkegren, J Tegnér, J Linkoping Univ Dept Phys & Measurement Technol S-58183 Linkoping Sweden Karolinska Inst Ctr Genom & Bioinformat S-17177 Stockholm Sweden

Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. this means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. this is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes;find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.

关键词： Algorithms Artificial Intelligence Bayes theorem Computer Simulation Gene Expression/physiology Gene Expression Profiling/methods Models, Genetic Neural Networks (Computer) pattern recognition, Automated/methods Proteome/metabolism Signal Transduction/genetics Signal Transduction/physiology

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Joint iapr international Workshops on 10th Structural, Syntactic, and Statistical pattern recognition, SSPR 2004 and 5th international Workshop on Statistical Techniques in pattern recognition, SPR 2004

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Joint iapr 10th international Workshop on Structural and Syntactic pattern recognition, SSPR 2004 and 5th international Workshop on Statistical Techniques in pattern recognition, SPR 2004

ISBN: (纸本)9783540225706

the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a demonstration on text data;the use of graph techniques for identifying objects and scenes in indoor building environments for mobile robots;graphical-based learning environments for pattern recognition;spectral analysis of complex laplacian matrices;a significant improvement of softassign with diffusion kernels;eigenspace method by autoassociative networks for object recognition;extraction of skeletal shape features using a visual attention operator;computing the cyclic edit distance for pattern classification by ranking edit paths;steady state random walks for path estimation;new variational framework for rigid-body alignment;an error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification;comparison of algorithms for web document clustering using graph representations of data;a syntactic pattern recognition approach to computer assisted translation;a general methodology for finite-state translation using alignments;a comparison of unsupervised shot classification algorithms for news video segmentation;diagnosis of lung nodule using the semivariogram function;distances between distributions;multiscale curvature assessment of postural deviations;learning people movement model from multiple cameras for behaviour recognition;a comparison of least squares and spectral methods for attributed graph matching and an auction algorithm for graph-based contextual correspondence matching.

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Self-formation supported by pattern recognition

Self-formation supported by pattern recognition

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6th international conference on Self-Formation theory and Applications

作者： Telksnys, L Inst Math & Informat Recognit Proc Dept LT-2600 Vilnius Lithuania

Artificial planar systems self-formation process supported by pattern recognition theory and methods are discussed. Concept possibilities to apply pattern recognition power for improving and control self-formation pro... 详细信息

ISBN: (纸本)3908450853

关键词： self-formation pattern recognition

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6th international Workshop on Document Analysis Systems, DAS 2004

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6th iapr international Workshop on Document Analysis Systems, DAS 2004

ISBN: (纸本)3540230602

the proceedings contain 53 papers. the special focus in this conference is on Digital Libraries, Historical Documents, Layout Analysis and Color Documents. the topics include: Document analysis systems for digital libraries;the trinity college Dublin 1872 online catalogue;a semantic-based system for querying personal digital libraries;adventures in attempting to reconstruct don Quixote;a segmentation-free recognition technique to assist old Greek handwritten manuscript OCR;automatic metadata retrieval from ancient manuscripts;a complete approach to the conversion of typewritten historical documents for digital archives;an adaptive binarization technique for low quality historical documents;segmentation of handwritten characters for digitalizing Korean historical documents;self-organizing maps and ancient documents;word grouping in document images based on voronoi tessellation;multi-component document image coding using regions-of-interest;physical layout analysis of complex structured Arabic documents using artificial neural nets;an integrated approach for automatic semantic structure extraction in document images;multi-view HAC for semi-supervised document image classification;configurable text stamp identification tool with application of fuzzy logic;layout and content extraction for PDF documents;automatic extraction of filled-in items from bank-check images;bleed-through removal from degraded documents using a color decorrelation method;colour map classification for archive documents;adaptive region growing color segmentation for text using irregular pyramid;preprocessing and segmentation of bad quality machine typed documents;information retrieval system for handwritten documents and multiscale handwriting characterization for writers’ classification.

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Understanding human-computer interactions in map revision

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Joint iapr 10th international Workshop on Structural and Syntactic pattern recognition, SSPR 2004 and 5th international Workshop on Statistical Techniques in pattern recognition, SPR 2004

作者： Zhou, Jun Bischof, Walter F. Caelli, Terry Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9783540225706

It is difficult to track, parse and model human-computer interactions during editing and revising of documents, but it is necessary if we are to develop automated technologies that will aid or replace humans. this paper introduces a system for accessing and recording a stream of events related to human actions in a real-time cartographic map revision system. the recorded events are parsed into a sequence of meaningful user actions and an action representation in XML format is generated. We also report results of experiments on predicting user actions such as view changes, edits, road tracking/production using hidden Markov models. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Human computer interaction

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Word–wise script identification from indian documents

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6th iapr international Workshop on Document Analysis Systems, DAS 2004

作者： Sinha, Suranjit Pal, Umapada Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Unit 203 B.T. Road Kolkata700 108 India

ISBN: (纸本)3540230602

In a country like India, a single text line of most of the official documents contains two different script words. Under two-language formula, the Indian documents are written in English and the state official language. For Optical Character recognition (OCR) of such a document page, it is necessary to separate different script words before feeding them to the OCRs of individual scripts. In this paper a robust technique is proposed to extract word-wise script identification from Indian doublet form documents. Here, at first, the document is segmented into lines and then the lines are segmented into words. Using different topological and structural features (like number of loops, headline feature, water reservoir concept based features, profile features, etc.) individual script words are identified from the documents. the proposed scheme is tested on 24210 words of different doublets and we received more than 97% accuracy, on average. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Optical character recognition

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