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pattern recognition for Subfamily Level Classification of GPCRs Using Motif Distillation and Distinguishing Power Evaluation

Pattern Recognition for Subfamily Level Classification of GP...

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7th international-Association-for-pattern-recognition (iapr) international conference on pattern recognition in bioinformatics (prib)

作者： Yavuz, Ahmet Sinan Ozer, Bugra Sezerman, Osman Ugur Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9783642341236

G protein coupled receptors (GPCRs) are one of the most prominent and abundant family of membrane proteins in the human genome. Since they are main targets of many drugs, GPCR research has grown significantly in recent years. However the fact that only few structures of GPCRs are known still remains as an important challenge. therefore, the classification of GPCRs is a significant problem provoked from increasing gap between orphan GPCR sequences and a small amount of annotated ones. this work employs motif distillation using defined parameters, distinguishing power evaluation method and general weighted set cover problem in order to determine the minimum set of motifs which can cover a particular GPCR subfamily. Our results indicate that in Family A Peptide subfamily, 91% of all proteins listed in GPCRdb can be covered by using only 691 different motifs, which can be employed later as an invaluable source for developing a third level GPCR classification tool.

关键词： g-protein coupled receptors data mining pattern recognition

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Aligning Discovered patterns from Protein Family Sequences

Aligning Discovered Patterns from Protein Family Sequences

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7th international-Association-for-pattern-recognition (iapr) international conference on pattern recognition in bioinformatics (prib)

作者： Lee, En-Shiun Annie Zhuang, Dennis Wong, Andrew K. C. Univ Waterloo Ctr Pattern Anal & Machine Intelligence Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783642341236

A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned pattern (AP) Cluster to discover potential functional segments in protein sequences. We apply our algorithm to identify and display the binding segments for the Cytochrome C. and Ubiquitin protein families. the resulting AP Clusters correspond to protein binding segments that surround the binding residues. When compared to the results from the protein annotation databases, PROSITE and pFam, ours are more efficient in computation and comprehensive in quality. the significance of the AP Cluster is that it is able to capture subtle variations of the binding segments in protein families. It thus could help to reduce time-consuming simulations and experimentation in the protein analysis.

关键词： Protein Analysis Protein Function Identification pattern Discovery pattern Clustering Hierarchical Clustering Motif Finding Local Alignment Approximate String Matching

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Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction

Application of the Multi-modal Relevance Vector Machine to t...

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7th international-Association-for-pattern-recognition (iapr) international conference on pattern recognition in bioinformatics (prib)

作者： Razin, Nikolay Sungurov, Dmitry Mottl, Vadim Torshin, Ivan Sulimova, Valentina Seredin, Oleg Windridge, David Moscow Inst Phys & Technol Moscow Russia Russian Acad Sci Ctr Comp Moscow Russia Tula State Univ Tula Russia Univ Surrey Guildford Surrey England

ISBN: (纸本)9783642341236

the aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. the commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones.

关键词： Protein secondary structure prediction machine learning multi-modal relational pattern recognition Relevance Vector Machine controlled selectivity of reference objects and object-comparison modalities

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2012年 7632 LNBI卷 V页

作者： Shibuya, Tetsuo Kashima, Hisashi Sese, Jun Ahmad, Shandar University of Tokyo Institute of Medical Science 4-6-1 Shirokanedai Minato-ku 108-8639 Tokyo Japan University of Tokyo Department of Mathematical Informatics 7-3-1 Hongo Bunkyo-ku 113-8654 Tokyo Japan Tokyo Institute of Technology Department of Computer Science 2-12-1 Ookayamama Meguro-ku 152-8550 Tokyo Japan National Institute of Biomedical Innovation 7-6-8 Saito-Asagi Suita 567-0085 Osaka Japan

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2011年 7036 LNBI卷 V-VI页

作者： Loog, Marco Wessels, Lodewyk Reinders, Marcel De Ridder, Dick Delft University of Technology Pattern Recognition and Bioinformatics Group Mekelweg 4 2628 CD Delft Netherlands Netherlands Cancer Institute Bioinformatics and Statistics Plesmanlaan 121 1066 CX Amsterdam Netherlands

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Epithelial Area Detection in Cytokeratin Microscopic Images Using MSER Segmentation in an Anisotropic Pyramid

Epithelial Area Detection in Cytokeratin Microscopic Images ...

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6th iapr international conference on pattern recognition in bioinformatics

作者： Smochina, Cristian Rogojanu, Radu Manta, Vasile Kropatsch, Walter Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Iasi Romania Med Univ Inst Pathophysiol & Allergy Res Vienna Austria Vienna Univ Technol Inst Comp Graph & Algorithms Pattern Recognit & Image Proc Grp A-1040 Vienna Austria

ISBN: (纸本)9783642248542

the objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. this problem is challenging due to the large variations of features of these components (size, shape, orientation or texture). In this paper we present an automatic technique to robustly delimit the epithelial area (crypts) in microscopic images taken from colon tissues sections marked with cytokeratin-8. the epithelial area is highlighted using the anisotropic diffusion pyramid and segmented using MSER+. the crypts separation and lumen detection is performed by imposing topological constraints about the epithelial layer distribution within the tissue and the round-like shape of the crypt. the evaluation of the proposed method is made by comparing the results with ground-truth segmentations.

关键词： Crypt segmentation Anisotropic diffusion pyramid MSER Biomedical imaging Pathology Microscopy Fluorescence microscopy Image analysis

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pattern recognition in High-Content Cytomics Screens for Target Discovery - Case Studies in Endocytosis

Pattern Recognition in High-Content Cytomics Screens for Tar...

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6th iapr international conference on pattern recognition in bioinformatics

作者： Cao, Lu Yan, Kuan Winkel, Leah de Graauw, Mado Verbeek, Fons J. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Leiden Univ Toxicol Leiden Amsterdam Ctr Drug Res Leiden Netherlands

ISBN: (纸本)9783642248542

Finding patterns in time series of images requires dedicated approaches for the analysis, in the setup of the experiment, the image analysis as well as in the pattern recognition. the large volume of images that are used in the analysis necessitates an automated setup. In this paper, we illustrate the design and implementation of such a system for automated analysis from which phenotype measurements can be extracted for each object in the analysis. Using these measurements, objects are characterized into phenotypic groups through classification while each phenotypic group is analyzed individually. the strategy that is developed for the analysis of time series is illustrated by a case study on EGFR endocytosis. Endocytosis is regarded as a mechanism of attenuating epidermal growth factor receptor (EGFR) signaling and of receptor degradation. Increasingly, evidence becomes available showing that cancer progression is associated with a defect in EGFR endocytosis. Functional genomics technologies combine high-throughput RNA interference with automated fluorescence microscopy imaging and multi-parametric image analysis, thereby enabling detailed insight into complex biological processes, like EGFR endocytosis. the experiments produce over half a million images and analysis is performed by automated procedures. the experimental results show that our analysis setup for high-throughput screens provides scalability and robustness in the temporal analysis of an EGFR endocytosis model.

关键词： phenotype measurement image analysis classification EGFR endocytosis

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Metabolic pathway inference from time series data: A non iterative approach

Metabolic pathway inference from time series data: A non ite...

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6th iapr international conference on pattern recognition in bioinformatics, prib 2011

作者： Astola, Laura Groenenboom, Marian Gomez Roldan, Victoria Van Eeuwijk, Fred Hall, Robert D. Bovy, Arnaud Molenaar, Jaap Biometris Wageningen University and Research Centre Droevendaalsesteeg 1 Wageningen Netherlands Netherlands Consortium for Systems Biology Amsterdam Netherlands Plant Research International Wageningen University Research Centre Wageningen Netherlands Centre for Biosystems Genomics P.O. Box 98 Wageningen Netherlands

ISBN: (纸本)9783642248542

In this article, we present a very fast and easy to implement method for reconstruction of metabolic pathways based on time series data. To model the metabolic reactions, we use the well-established setting of ordinary differential equations. In the present article we consider a network leading to the accumulation of quercetin-glycosides in tomato (Solanum lycopersicum). Quercetin belongs to a group of plant secondary metabolites, generally referred to as flavonoids, which are extensively being studied for their variety of important functions in plants as well as for their potentially health-promoting effects on human. We use time series measurements of metabolite concentrations of quercetin derivatives. In the present setting, the observed concentrations are the variables and the reaction rates are the unknown parameters. A standard method is to solve the parameters by reverse engineering, where the ordinary differential equations (ODE) are solved repeatedly, resulting in impractical computation times. We use an alternative method that estimates the parameters by least squares minimization, and which is, in the order of hundred times faster than the iterative method. Our reconstruction method can incorporate an arbitrary a priori known network structure as well as positivity constraints on the reaction rates. In this way we can avoid over-fitting, which is another often encountered problem in network reconstruction, and thus obtain better estimates for the parameters. We test the presented method by reconstructing artificial networks and compare it with the more conventional method in terms of residuals between the observed and fitted concentrations, computing times and the proportion of correctly identified edges in the network. Finally we exploit this fast method to statistically infer the kinetic constants in the flavonoid pathway. We remark that the method as such is not limited to metabolic network reconstructions, but can be used with any type of time-series

关键词： Ordinary differential equations

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Discrete Geometry for Computer Imagery - 16th iapr international conference, DGCI 2011, Proceedings

Discrete Geometry for Computer Imagery - 16th IAPR Internati...

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16th iapr international conference on Discrete Geometry for Computer Imagery, DGCI 2011

ISBN: (纸本)9783642198663

the proceedings contain 43 papers. the topics discussed include: a probabilistic grouping principle to go from pixels to visual structures;an arithmetic and combinatorial approach to three-dimensional discrete lines;smooth 2D coordinate systems on discrete surfaces;an improved Riemannian metric approximation for graph cuts;associating cell complexes to four dimensional digital objects;hierarchic Euclidean skeletons in cubical complexes;a unified topological framework for digital imaging;isthmus-based 6-directional parallel thinning algorithms;quasi-linear transformations, numeration systems and fractals;path-based distance with varying weights and neighborhood sequences;sparse object representations by digital distance functions;efficient robust digital hyperplane fitting with bounded error;circular arc reconstruction of digital contours with chosen Hausdorff error;and an error bounded tangent estimator for digitized elliptic curves.

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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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