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pattern recognition in bioinformatics: 9th iapr international conference, prib 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings 1

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丛书名： Lecture Notes in Computer Science 8626 Lecture Notes in bioinformatics

2014年

作者： Matteo Comin, Lukas Käll, Elena Marchiori, Alioune Ngom, Jagath Rajapakse (eds.)

ISBN: (数字)9783319091921

ISBN: (纸本)9783319091914;9783319091921

this book constitutes the refereed proceedings of the 8th iapr international conference on pattern recognition in bioinformatics, prib 2014, held in Stockholm, Sweden in August 2014. the 9 revised full papers and 9 revised short papers presented were carefully reviewed and selected from 29 submissions. the focus of the conference was on the latest Research in pattern recognition and Computational Intelligence-Based Techniques Applied to Problems in bioinformatics and Computational Biology.

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

9th IAPR TC3 International Workshop on Artificial Neural Net...

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

ISBN: (纸本)9783030583088

the proceedings contain 24 papers. the special focus in this conference is on Artificial Neural Networks in pattern recognition. the topics include: Named entity disambiguation at scale;geometric attention for prediction of differential properties in 3d point clouds;preface;deep learning methods for image guidance in radiation therapy;how (not) to measure bias in face recognition networks;feature extraction: A time window analysis based on the x-ite pain database;pain intensity recognition - an analysis of short-time sequences in a real-world scenario;a deep learning approach for efficient registration of dual view mammography;deep transfer learning for texture classification in colorectal cancer histology;applications of generative adversarial networks to dermatologic imaging;typing plasmids with distributed sequence representation;kp-yolo: A modification of yolo algorithm for the keypoint-based detection of qr codes;using mask r-cnn for image-based wear classification of solid carbide milling and drilling tools;a hybrid deep learning approach for forecasting air temperature;intentional image similarity search;using cnns to optimize numerical simulations in geotechnical engineering;going for 2d or 3d? investigating various machine learning approaches for peach variety identification;a transfer learning end-to-end arabic text-to-speech (tts) deep architecture;ml-based trading models: An investigation during covid-19 pandemic crisis;innvestigate-gui - explaining neural networks through an interactive visualization tool;structured (de)composable representations trained with neural networks;long distance relationships without time travel: Boosting the performance of a sparse predictive autoencoder in sequence modeling.

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pattern recognition in bioinformatics - 9th iapr international conference, prib 2014, Proceedings

Pattern Recognition in Bioinformatics - 9th IAPR Internation...

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9th iapr international conference on pattern recognition in bioinformatics, prib 2014

ISBN: (纸本)9783319091914

the proceedings contain 11 papers. the topics discussed include: acquiring decision rules for predicting AMES-negative hepatocarcinogens using chemical-chemical interactions;using topology information for protein-protein interaction prediction;biases of drug-target interaction network data;logol: expressive pattern matching in sequences. application to ribosomal frameshift modeling;evolutionary algorithm based on new crossover for the biclustering of gene expression data;SFFS-SW: a feature selection algorithm exploring the small-world properties of GNs;CytomicsDB: a metadata-based storage and retrieval approach for high-throughput screening experiments;CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory Networks from expression data using random forest;supervised selective kernel fusion for membrane protein prediction;and analysis of miRNA expression profiles in breast cancer using biclustering.

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A novel saccade signals detection algorithm for EOG-based Human Activity recognition 9th

A novel saccade signals detection algorithm for EOG-based Hu...

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9th international conference on bioinformatics and Biomedical Engineering (iCBBE)

作者： Lv, Z. Guan, J. N. Zhou, B. Y. Wu, X. P. Anhui Univ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China Anhui Univ Coinnovat Ctr Informat Supply & Assurance Technol Hefei Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781315683010;9781138027848

A research on the relationship between eye movements and human behavior is a hot topic in the field of Human Activity recognition (HAR). In this paper, a novel saccade signals detection algorithm for EOG-based HAR, which aims to improve the performance of HAR system, was proposed. In the proposed algorithm, Common Spatial pattern (CSP) was utilized to build spatial filters, and then use it to process original multi-channel EOG signals. Consequently, feature parameters of different saccade signals can be acquired. To valid the performance of the proposed algorithm, a linear Support Vector Machine (SVM) was chosen. In lab environment, four types of saccade signals corresponding to up, down, left and right were used as analysis objects. Experimental results show that the accuracy recognition ratio is about 97.7%, which reveal that the proposed algorithm has a good classification performance in saccade signals analysis.

关键词： EOG saccade signals Common Spatial pattern (CSP) joint approximate diagonalization Support Vector Machine (SVM)

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the relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

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BMC bioinformatics 2015年第4期16卷 1-14页

作者： Ibrahim, Zina M. Ngom, Alioune Kings Coll London Inst Psychiat London SE5 8AF England NIHR Biomed Res Ctr Mental Hlth South London England Biomed Res Unit Dementia South London England Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Maudsley NHS Fdn Trust London SE5 8AZ England

Background: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results: We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions: Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

关键词： Protein Complexes Weighted Networks Functional Modules Network Clustering Criterion Protein Interaction Networks

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Guest Editorial: pattern recognition in bioinformatics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2014年第3期11卷 498-499页

作者： Marchiori, Elena Ngom, Alioune Acharya, Raj Radboud Univ Nijmegen Fac Sci Dept Comp Sci NL-6525 AJ Nijmegen Netherlands Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Penn State Univ Coll Engn Dept Comp Sci & Engn University Pk PA 16802 USA

Development and application of pattern recognition techniques in the field of bioinformatics is of utmost importance for gaining new insights about phenomena in life sciences through the analysis of biological data. In this special section, three research manuscripts in their significantly extended form were selected from the papers presented at the Eighth iapr international conference on pattern recognition in bioinformatics (prib 2013), which was held in Nice, France.

关键词： Special Issues And Sections Meetings bioinformatics Genomics pattern recognition Feature Extraction Semantics Proteins

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SFFS-SW: A Feature Selection Algorithm Exploring the Small-World Properties of GNs

SFFS-SW: A Feature Selection Algorithm Exploring the Small-W...

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9th iapr international conference on pattern recognition in bioinformatics (prib)

作者： da Rocha Vicente, Fabio Fernandes Lopes, Fabricio Martins Fed Univ Technol Curitiba Parana Brazil

ISBN: (纸本)9783319091921;9783319091914

In recent years, several methods for gene networks (GNs) inference from expression data have been developed. Also, models of data integration (as protein-protein and protein-DNA) are nowadays broadly used to face the problem of few amount of expression data. Moreover, it is well known that biological networks conserve some topological properties. the small-world topology is a common arrangement in nature found both in biological and non-biological phenomena. However, in general this information is not used by GNs inference methods. In this work we proposed a new GNs inference algorithm that combines topological features and expression data. the algorithm outperforms the approach that uses only expression data both in accuracy and measures of recovered network.

关键词： small-world gene networks feature selection graph theory pattern recognition bioinformatics

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Logol: Expressive pattern Matching in Sequences. Application to Ribosomal Frameshift Modeling

Logol: Expressive Pattern Matching in Sequences. Application...

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9th iapr international conference on pattern recognition in bioinformatics (prib)

作者： Belleannee, Catherine Sallou, Olivier Nicolas, Jacques Univ Rennes 1 Irisa Inria F-35042 Rennes France

ISBN: (纸本)9783319091921;9783319091914

Most of the current practice of pattern matching tools is oriented towards finding efficient ways to compare sequences. this is useful but insufficient: as the knowledge and understanding of some functional or structural aspects of living systems improve, analysts in molecular biology progressively shift from mere classification tasks to modeling tasks. People need to be able to express global sequence architectures and check various hypotheses on the way their sequences are structured. It appears necessary to offer generic tools for this task, allowing to build more expressive models of biological sequence families, on the basis of their content and structure. this article introduces Logol, a new application designed to achieve pattern matching in possibly large sequences with customized biological patterns. Logol consists in both a language for describing patterns, and the associated parser for effective pattern search in sequences (RNA, DNA or protein) with such patterns. the Logol language, based on an high level grammatical formalism, allows to express flexible patterns (with mispairings and indels) composed of both sequential elements (such as motifs) and structural elements (such as repeats or pseudoknots). Its expressive power is presented through an application using the main components of the language : the identification of -1 programmed ribosomal frameshifting (PRF) events in messenger RNA sequences. Logol allows the design of sophisticated patterns, and their search in large nucleic or amino acid sequences. It is available on the GenOuest bioinformatics platform at http://***. the core application is a command-line application, available for different operating systems. the Logol suite also includes interfaces, e. g. an interface for graphically drawing the pattern.

关键词： Molecular biology

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9th Brazilian Symposium on bioinformatics, BSB 2014

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19th Iberoamerican Congress on pattern recognition, CIARP 2014

ISBN: (纸本)9783319125671

the proceedings contain 126 papers. the special focus in this conference is on biometrics technology, evolutionary multi-objective optimization, searching for patterns in imbalanced data, lattice neural networks, metaheuristics for multi-objective Optimization, pattern recognition and lattice neural networks theory and applications. the topics include: An introduction to evolutionary multi-objective optimization with some applications in pattern recognition;rotation-invariant nonlinear filters design;estimation of bubble size distribution based on power spectrum;a robust tracking algorithm based on HOGs descriptor;real time hardware accelerator for image filtering;self-adaptive skin segmentation in color images;a comparison of fused segmentation algorithms for iris verification;angle estimation using Hahn moments for image analysis;searching for patterns in imbalanced data;on the use of locality sensitive hashing for audio following;from speech quality measures to speaker recognition performance;artificial neural networks for acoustic lung signals classification;feature analysis for audio classification;assessing cross-cut shredded document assembly;person reidentification and recognition in video;automatic classification of coating epithelial tissue;scalable feature extraction for visual surveillance;thermal face recognition using local patterns;evaluation of key point descriptors for gender recognition;conformal geometric method for voting;a fast pavement location approach for autonomous car navigation;fast edge detection in RGB-D images;summarization of videos by image quality assessment;a deformable model to search characteristic facial points;lifting filters adjustment for lossless image compression applications;morphological change of a scene employing synthetic multispectral and panchromatic images and data fusion approach for employing multiple classifiers to improve lake shoreline analysis.

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Introducing an experimental framework in C# for fingerprint recognition

Introducing an experimental framework in C# for fingerprint ...

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6th Mexican conference on pattern recognition, MCPR 2014

作者： Medina-Pérez, Miguel Angel Loyola-González, Octavio Gutierrez-Rodríguez, Andres Eduardo García-Borroto, Milton Altamirano-Robles, Leopoldo Instituto Nacional de Astrofísica Óptica Y Electrónica Luis Enrique Erro No. 1 Sta. María Tonanzintla Puebla C.P. 72840 Mexico Centro de Bioplantas Universidad de Ciego de Ávila Carretera a Morón km 9 Ciego de Ávila Cuba Instituto Superior Politécnico José A. Echeverría C. 114 No. 11901 e/ Ciclovia y Rotonda Apdo. 6028 Marianao Habana Cuba

ISBN: (纸本)9783319074900

In this paper, a framework for fingerprint recognition is introduced. the framework contains several algorithms for fingerprint matching and feature extraction, as well as the evaluation protocol for several fingerprint verification competitions. All the algorithms are implemented in C# providing the source code which researchers can review, reuse or modify. In order to show the framework relevance, an experimental comparison among different matching algorithms is presented. © 2014 Springer international Publishing.

关键词： pattern matching

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