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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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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...

引用

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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Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression

Machine Learning Scoring Functions Based on Random Forest an...

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

作者： Ballester, Pedro J. European Bioinformat Inst Cambridge England

ISBN: (纸本)9783642341236

Accurately predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. the scoring functions that attempt such computational prediction exploiting structural data are essential for analysing the outputs of Molecular Docking, which is in turn an important technique for drug discovery, chemical biology and structural biology. Conventional scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that characterise the complex and its predicted binding affinity. the inherent problem of this approach is in the difficulty of explicitly modelling the various contributions of intermolecular interactions to binding affinity. Recently, a new family of 3D structure-based regression models for binding affinity prediction has been introduced which circumvent the need for modelling assumptions. these machine learning scoring functions have been shown to widely outperform conventional scoring functions. However, to date no direct comparison among machine learning scoring functions has been made. Here the performance of the two most popular machine learning scoring functions for this task is analysed under exactly the same experimental conditions.

关键词： molecular docking scoring functions machine learning chemical informatics structural bioinformatics

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Multiple Tree Alignment with Weights Applied to Carbohydrates to Extract Binding recognition patterns

Multiple Tree Alignment with Weights Applied to Carbohydrate...

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

作者： Hosoda, Masae Akune, Yukie Aoki-Kinoshita, Kiyoko F. Soka Univ Fac Engn Dept Bioinformat Hachioji Tokyo 1928577 Japan

ISBN: (纸本)9783642341236

the purpose of our research is the elucidation of glycan recognition patterns. Glycans are composed of monosaccharides and have complex structures with branches due to the fact that monosaccharides have multiple potential binding positions compared to amino acids. Each monosaccharide can potentially be bound by up to five other monosaccharides, compared to two for any amino acid. Glycans are often bound to proteins and lipids on the cell surface and play important roles in biological processes. Lectins in particular are proteins that recognize and bind to glycans. In general, lectins bind to the terminal monosaccharides of glycans on glycoconjugates. However, it is suggested that some lectins recognize not only terminal monosaccharides, but also internal monosaccharides, possibly influencing the binding affinity. Such analyses are difficult without novel bioinformatics techniques. thus, in order to better understand the glycan recognition mechanism of such biomolecules, we have implemented a novel algorithm for aligning glycan tree structures, which we provide as a web tool called MCAW (Multiple Carbohydrate Alignment with Weights). From our web tool, we have analyzed several different lectins, and our results could confirm the existence of well-known glycan motifs. Our work can now be used in several other analyses of glycan structures, such as in the development of glycan score matrices as well as in state model determination of probabilistic tree models. therefore, this work is a fundamental step in glycan pattern analysis to progress glycobiology research.

关键词： glycomics glycans bioinformatics multiple tree alignment algorithm

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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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Analysis of Preprocessing Techniques for Latin Handwriting recognition

Analysis of Preprocessing Techniques for Latin Handwriting R...

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13th international conference on Frontiers in Handwriting recognition (ICFHR)

作者： Pesch, Hendrik Hamdani, Mahdi Forster, Jens Ney, Hermann Rhein Westfal TH Aachen Chair Comp Sci 6 Human Language Technol & Pattern Recognit Grp D-52056 Aachen Germany

ISBN: (纸本)9780769547749;9781467322621

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. this pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. the Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. the results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.

关键词： Handwriting recognition Hidden Markov Models Preprocessing

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Moment-based Image Normalization for Handwritten Text recognition

Moment-based Image Normalization for Handwritten Text Recogn...

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13th international conference on Frontiers in Handwriting recognition (ICFHR)

作者： Kozielski, Michal Forster, Jens Ney, Hermann Rhein Westfal TH Aachen Chair Comp Sci 6 Human Language Technol & Pattern Recognit Grp D-52056 Aachen Germany

ISBN: (纸本)9780769547749;9781467322621

In this paper, we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text. Image moments provide robust estimates for text characteristics such as size and position of words within an image. For handwriting recognition the normalization procedure is applied to image slices independently. Additionally, a novel moment-based algorithm for line-thickness normalization is presented. the proposed normalization methods are evaluated on the RIMES database of French handwriting and the IAM database of English handwriting. For RIMES we achieve an improvement from 16.7% word error rate to 13.4% and for IAM from 46.6% to 37.3%.

关键词： document image processing handwriting recognition handwritten character recognition image thinning

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Application of Bayesian Networks for pattern recognition: Character recognition Case

Application of Bayesian Networks for Pattern Recognition: Ch...

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6th international conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

作者： Jayech, Khlifia Mahjoub, Mohamed Ali Ghanmi, Nabil Natl Engn Sch Sousse Res Unit SAGE Sahloul 4054 Sousse Tunisia

ISBN: (纸本)9781467316583

pattern recognition is a wide field in progress. In particular, handwriting recognition has known a great development in the recent years. Several solutions have been directed towards the use of Bayesian networks, which have shown their ability to solve complex problems in many areas, and that is thanks to their ability to model inaccuracies, which are lacunae highly present in the manuscript field. In this paper, we recall the basics of these networks and the difficulties come across in their learning and inference algorithms to make a good decision. We present a state of using the BNs and especially RBDs in the pattern recognition and more exactly in the character recognition. We show, through the various considered works, the contribution of this technique in solving the limitations of the Markov models and its ability to represent efficiently the temporal notion and the dependencies between the variables during the writing process. Moreover, we retain the recorded limitations and some development perspectives.

关键词： pattern recognition dynamic Bayesian network OCR SRC

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A tool for hand-sign recognition

A tool for hand-sign recognition

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4th Mexican conference on pattern recognition, MCPR 2012

作者： Rios Soria, David J. Schaeffer, Satu Elisa San Nicolás de los Garza NL Mexico

ISBN: (纸本)9783642311482

We present a software tool created for human-computer interaction based on hand gestures. the underlying algorithm utilizes computer vision techniques. the tool is able to recognize in real-time six different hand signals, captured using a web cam. Experiments conducted to evaluate the system performance are reported. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： pattern recognition

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Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition

Evolving spiking neural networks for spatio-and spectro-temp...

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2012 6th IEEE international conference Intelligent Systems, IS 2012

作者： Kasabov, Nikola Knowledge Engineering and Discovery Research Institute-KEDRI Auckland University of Technology and Institute for Neuroinformatics-INI ETH and University of Zurich Auckland New Zealand

ISBN: (纸本)9781467327824

this paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted. © 2012 IEEE.

关键词： Neural networks

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