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7th iapr international conference on pattern recognition in bioinformatics, prib 2012

ISBN: (纸本)9783642341229

the proceedings contain 24 papers. the topics discussed include: robust community detection methods with resolution parameter for complex detection in protein-protein interaction networks;machine learning scoring functions based on random forest and support vector regression;multiple tree alignment with weights applied to carbohydrates to extract binding recognition patterns;improving the portability and performance of *** - a dynamic RNA visualization software;a novel machine learning approach for detecting the brain abnormalities from MRI structural images;an algorithm to assemble gene-protein-reaction associations for genome-scale metabolic model reconstruction;a machine learning and chemometrics assisted interpretation of spectroscopic data - a NMR-based metabolomics platform for the assessment of Brazilian Propolis;and application of the multi-modal relevance vector machine to the problem of protein secondary structure prediction.

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pattern recognition in bioinformatics 2012

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丛书名： Lecture Notes in Computer Science

2012年

作者： Tetsuo Shibuya Hisashi Kashima Jun Sese Shandar Ahmad

ISBN: (数字)9783642341236

ISBN: (纸本)9783642341229

this book constitutes the refereed proceedings of the 7th international conference on pattern recognition in bioinformatics, prib 2012, held in Tokyo, Japan, in November 2012. the 24 revised full papers presented were carefully reviewed and selected from 33 submissions. their topics are widely ranging from fundamental techniques, sequence analysis to biological network analysis. the papers are organized in topical sections on generic methods, visualization, image analysis, and platforms, applications of pattern recognition techniques, protein structure and docking, complex data analysis, and sequence analysis.

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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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pattern recognition in bioinformatics 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Tetsuo Shibuya Hisashi Kashima Jun Sese Shandar Ahmad

ISBN: (数字)9783642341236

ISBN: (纸本)9783642341229

关键词： Computational Biology/bioinformatics Health Informatics pattern recognition Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity Artificial Intelligence

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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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7th iapr TC3 Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2016

7th IAPR TC3 Workshop on Artificial Neural Networks in Patte...

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7th iapr TC3 Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2016

ISBN: (纸本)9783319461816

the proceedings contain 27 papers. the special focus in this conference is on Learning Algorithms, Architectures and Applications. the topics include: A spiking neural network for personalised modelling of electrogastrography;improving generalization abilities of maximal average margin classifiers;finding small sets of random Fourier features for shift-invariant kernel approximation;incremental construction of low-dimensional data representations;soft-constrained nonparametric density estimation with artificial neural networks;interpretable classifiers in precision medicine;on the evaluation of tensor-based representations for optimum-path forest classification;on the harmony search using quaternions;learning parameters in deep belief networks through firefly algorithm;towards effective classification of imbalanced data with convolutional neural networks;on CPU performance optimization of restricted Boltzmann machine and convolutional RBM;comparing incremental learning strategies for convolutional neural networks;approximation of graph edit distance by means of a utility matrix;time series classification in reservoir- and model-space;objectness scoring and detection proposals in forward-looking sonar images with convolutional neural networks;background categorization for automatic animal detection in aerial videos using neural networks;predictive segmentation using multichannel neural networks in Arabic OCR system;quad-tree based image segmentation and feature extraction to recognize online handwritten bangla characters;using radial basis function neural networks for continuous and discrete pain estimation from bio-physiological signals;emotion recognition in speech with deep learning architectures and on gestures and postural behavior as a modality in ensemble methods.

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Syntactic pattern recognition from Observations: A Hybrid Technique

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7th international conference on Intelligent Computing (ICIC)

作者： Di Lecce, Vincenzo Calabrese, Marco Polytech Bari DIASS Taranto Italy

ISBN: (纸本)9783642245527;9783642245534

this paper presents a novel technique for automated learning from observations. the technique arranges in a row four traditional pattern recognition approaches (numeric, logic, statistical and finally syntactic) within a unifying framework. Each processing step is conceived as a transformation of the input dataset from one state to another. the proposed technique considers measurable observations as inputs and produces a set of formal rules, i.e., a grammar, as final output. To this end, a four-state grammar induction process is described in detail by means of a step-by-step example. As a proof-of-concept for the feasibility of the proposal, references to early experimental validations are given. Finally, possible comparison with other well-known approaches are discussed.

关键词： Patter recognition grammar induction IF thEN rules

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Joint iapr international Workshops on Structural and Syntactic pattern recognition and Statistical Techniques in pattern recognition, SSPR and SPR 1998.

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7th Joint iapr international Workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international Workshop on Statistical Techniques in pattern recognition, SPR 1998

ISBN: (纸本)3540648585

the proceedings contain 113 papers. the special focus in this conference is on Structural Matching, Grammatical Inference and recognition of 2D and 3D Objects. the topics include: Error-tolerant graph matching;semantic content based image retrieval using object-process diagrams;pattern recognition methods in image and video databases;efficient matching with invariant local descriptors;integrating numerical and syntactic learning models for pattern recognition;synthesis of function-described graphs;marked subgraph isomorphism of ordered graphs;distance evaluation in pattern matching based on frontier topological graph;syntactic interpolation of fractal sequences;minimizing the topological structure of line images;genetic algorithms for structural editing;the noisy subsequence tree recognition problem;object recognition from large structural libraries;acquisition of 2-d shape models from scenes with overlapping objects using string matching;a taxonomy of occlusion in view signature ii representations;a survey of non-thinning based vectorization methods;a benchmark for raster to vector conversion systems;network-based recognition of architectural symbols;recovering image structure by model-based interaction map;an improved scheme to fingerprint classification;character recognition with k-head finite array automata;using semantics in matching cursive chinese handwritten annotations;concavity detection using a binary mask-based approach;structural indexing of line pictures with feature generation models;nonlinear covariance for multi-band image data;a neural network for image smoothing and segmentation;prototyping structural descriptions and neural network based learning of local compatibilities for segment grouping.

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