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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology"

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Ensembles of classifiers and their aplication to bioinformatics problems

Ensembles of classifiers and their aplication to bioinformat...

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Annual ieee symposium on Computer-Based Medical Systems

作者： Vasile Palade Oxford University Computing Laboratory University of Oxford Oxford UK

Summary form only given. Machine Learning has become a very popular approach in addressing problems in the computational biology and bioinformatics area. In addition, multi-classifier systems have also gained popularity among researchers working in machine learning and applications for their ability to fuse together multiple models and obtain better overall accuracy and classification results. This talk is concerned with current issues in the design of multi-classifier systems and presents some multi-classifier developments for several bioinformatics problems. The talk will first present an overview and current status of machine learning methods in bioinformatics and computational biology. The talk will then bring in some important issues in building ensembles of classifiers, with a focus on the diversity and combination of individual classifiers. Few diversification and combination schemes are presented along with guidelines for the selection of different training paradigms and performance metrics, based on the properties and distribution of the data. Then, the presentation will proceed with introducing our computational intelligence based multi-classifier developments for solving several bioinformatics problems, such as recognizing sequences in DNA strings, micro-array gene expression data analysis, protein structure prediction. The talk will also present related machine learning issues in developing such systems, such as learning from imbalanced datasets and using appropriate performance metrics for model selection and evaluation. The presented approaches and results will advocate that ensembles of classifiers can be used as effective modelling tools in solving challenging bioinformatics problems.

关键词： bioinformatics Machine learning Biological system modeling computational modeling Buildings Educational institutions computational biology

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Advances in Neural Networks - ISNN 2010 - 7th International symposium on Neural Networks, ISNN 2010, Proceedings

Advances in Neural Networks - ISNN 2010 - 7th International ...

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7th International symposium on Neural Networks, ISNN 2010

ISBN: (纸本)3642132774

The proceedings contain 170 papers. The topics discussed include: stimulus-dependent noise facilitates tracking performances of neuronal networks;range parameter induced bifurcation in a single neuron model with delay-dependent parameters;messenger RNA polyadenylation site recognition in green alga;a study to neuron ensemble of cognitive cortex ISI coding represent stimulus;STDP within NDS neurons;support vector regression and ant colony optimization for grid resources prediction;an improved kernel principal component analysis for large-scale data set;optimization of training samples with affinity propagation algorithm for multi-class SVM classification;an effective support vector data description with relevant metric learning;and a support vector machine (SVM) classification approach to heart murmur detection.

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Advances in Neural Networks - ISNN 2010 - 7th International symposium on Neural Networks, ISNN 2010, Proceedings

Advances in Neural Networks - ISNN 2010 - 7th International ...

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7th International symposium on Neural Networks, ISNN 2010

ISBN: (纸本)3642133177

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ACOPHY: A simple and general ant colony optimization approach for phylogenetic tree reconstruction

ACOPHY: A simple and general ant colony optimization approac...

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7th International Conference on Swarm intelligence, ANTS 2010

作者： Dinh, Huy Q. Minh, Bui Quang Huan, Hoang Xuan Von Haeseler, Arndt Center for Integrative Bioinformatics Medical University of Vienna University of Veterinary Medicine Vienna Austria Gregor Mendel Institute of Molecular Plant Biology Austrian Academy of Sciences Vienna Austria Faculty of Information Technology College of Technology Hanoi Viet Nam

ISBN: (纸本)3642154603

We introduce ACOPHY, a novel framework to apply Ant Colony Optimization (ACO) for phylogenetic reconstruction. ACOPHY overcomes a main drawback of other attempts to reconstruct phylogenies by defining a compact ACO graph that is nicely coupled with the tree space. The proposed graph allows the ants to walk globally through the tree space. Thus, ACOPHY can be generally applied to all well-known optimality criteria in phylogenetics. We compared ACOPHY with the traditional phylogenetic method PHYLIP and obtained slightly better results. This is promising since our current implementation of ACOPHY is still at the proof of concept stage. We list a number of points where ACOPHY can be improved. Once the improvements are integrated, we hope for competitive performance against other recent phylogenetic inference methods. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Ant colony optimization

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String matching with variable length gaps

String matching with variable length gaps

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17th International symposium on String Processing and Information Retrieval, SPIRE 2010

作者： Bille, Philip Li Gørtz, Inge Vildhøj, Hjalte Wedel Wind, David Kofoed Technical University of Denmark Denmark

ISBN: (纸本)9783642163203

We consider string matching with variable length gaps. Given a string T and a pattern P consisting of strings separated by variable length gaps (arbitrary strings of length in a specified range), the problem is to find all ending positions of substrings in T that match P. This problem is a basic primitive in computational biology applications. Let m and n be the lengths of P and T, respectively, and let k be the number of strings in P. We present a new algorithm achieving time O((n + m)logk + α) and space O(m + A), where A is the sum of the lower bounds of the lengths of the gaps in P and α is the total number of occurrences of the strings in P within T. Compared to the previous results this bound essentially achieves the best known time and space complexities simultaneously. Consequently, our algorithm obtains the best known bounds for almost all combinations of m, n, k, A, and α. Our algorithm is surprisingly simple and straightforward to implement. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Artificial intelligence

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Estimating the Expression of Transcript Isoforms from mRNA-Seq via Nonnegative Least Squares

Estimating the Expression of Transcript Isoforms from mRNA-S...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Hyunsoo Kim Yingtao Bi Ramana V. Davuluri Center of Systems and Computational Biology Wistar Institute Philadelphia PA USA

mRNA-Seq is an emerging massive parallel sequencing based technology for identification and quantification of gene transcripts. Although gene level expression can easily be estimated from mRNA-Seq data, estimating the isoform level expression poses serious problems, and appropriate methods are required. In this paper, we introduce a mathematical method to estimate transcript isoform concentrations, i.e. transcript isoform expression estimation via nonnegative least squares (TIEE/NLS).

关键词： Least squares approximation bioinformatics Genomics Cancer Cells (biology) Clustering algorithms Data analysis Biomedical engineering Bismuth computational biology

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How to Fold Amino Acid Interaction Networks by computational intelligence Methods

How to Fold Amino Acid Interaction Networks by Computational...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Omar Gaci LITIS Laboratory-EA4108 Le Havre University Le Havre France

In this paper we present the main steps to fold amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We begin by summarize relative works about this type of graphs to describe their topological properties. Then, we propose a genetic algorithm which reconstructs the secondary structure motifs. We continue our folding process with an ant colony approach. We guide the ant system to the tertiary structure relying on a probability that two amino acids interact as a function of their physico-chemical properties.

关键词： Amino acids computational intelligence Proteins bioinformatics Genetic algorithms Genomics Biomedical engineering Laboratories Humans Biological information theory

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Evolving Lose-Checkers players using genetic programming

Evolving Lose-Checkers players using genetic programming

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ieee symposium on computational intelligence and Games, CIG

作者： Amit Benbassat Moshe Sipper Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel

We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Lose Checkers. Our system implements strongly typed GP trees, explicitly defined introns, local mutations, and multi-tree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reservoir for possible future use. Multi-tree individuals are implemented by a method inspired by structural genes in living organisms, whereby we take a single tree describing a state evaluator and split it.

关键词： Games Humans Genomics bioinformatics Receivers Computers

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An Accurate Prediction Method for Protein Structural Class from Signal Patterns of NMR Spectra in the Absence of Chemical Shift Assignments

An Accurate Prediction Method for Protein Structural Class f...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Hiromi Arai Naoya Tochio Tsuyoshi Kato Takanori Kigawa Masayuki Yamamura Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology Yokohama Japan RIKEN Systems and Structural Biology Center Yokohama Japan Center of Informational Biology Ochanomizu University Bunkyo Tokyo Japan

The structural class information about a protein is important to understand its biological properties. NMR is one of the most powerful tools to obtain structural information of proteins in atomic resolution. However, an analysis of protein three-dimensional structure from NMR spectra usually requires laborious chemical shift assignment. We developed a new method for predicting the protein structural class directly from the NMR spectra without any chemical shift assignment. The results show that our method outperforms the methods using current secondary structure prediction.

关键词： Prediction methods Nuclear magnetic resonance Protein engineering Chemical technology Chemical analysis Hydrogen Spine Chemical engineering bioinformatics Biomedical engineering

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Improved Immune Genetic Algorithm for Clustering Protein-Protein Interaction Network

Improved Immune Genetic Algorithm for Clustering Protein-Pro...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Hamid Ravaee Ali Masoudi-Nejad Saeed Omidi Ali Moeini Department of Algorithms and Computations College of Engineering University of Tehran Iran Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics and Center of Excellence in Biomathematics University of Tehran Iran

Clustering protein-protein interaction network aims to find functional modules and protein complexes. There are many computational graph clustering methods that are used in this field, but few of them are intelligent computational methods. In this paper, we present a novel improved immune genetic algorithm to find dense subgraphs based on efficient vaccination method, variable-length antibody schema definition and new local and global mutations.

关键词： Genetic algorithms Proteins Biological system modeling Circadian rhythm Power system modeling Biochemistry Mathematical model Systems biology Biomedical engineering USA Councils

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