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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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Comparison of discrimination methods for peptide classification in tandem mass spectrometry

Comparison of discrimination methods for peptide classificat...

引用

ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Bonner, A Liu, H Univ Toronto Dept Comp Sci Toronto ON M5S 3G4 Canada

ISBN: (纸本)0780387287

Proteomics-the direct analysis of the expressed protein components of a cell-is critical to our understanding of cellular biological processes. Key insights into the action and effects of a disease can be obtained by comparison of the expression of the expressed proteins in normal versus diseased tissue. Tandem mass spectrometry(MS/MS) of peptides is a central technology for Proteomics, enabling the identification of thousands of peptides from a complex mixture. With the increasing acquisition rate of tandem mass spectrometers, there is an increasing potential to solve important biological problems by applying data-mining and machine-learning techniques to MS/MS data. These problems include (i) estimating the levels of the thousands of proteins in a tissue sample, (ii) predicting the intensity of the peaks in a mass spectrum, and (iii) explaining why different peptides from the same protein have different peak intensities. In other works, we have focussed on the first two problems. In this paper, we focus on the last problem. In particular, we try to explain why some peptides produce peaks of great intensity, while others produce peaks of low intensity, and we treat this as a classification problem. That is, we experimentally evaluate and compare a variety of discrimination methods for classifying peptides into those that produce high-intensity peaks and those that produce low-intensity peaks. The methods considered include K-Nearest Neighbours (KNN), Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Naive Bayes, and Hidden Markov Models (HMMs). Experiments using these methods were conducted on three real-world datasets derived from tissue samples of Mouse. The methods were then evaluated using ROC curves and cross validation.

关键词： data mining machine learning proteomics bioinformatics tandem mass spectrometry peptide classification

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A comparison of evolved finite state classifiers and interpolated Markov models for improving PCR primer design

A comparison of evolved finite state classifiers and interpo...

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Proceedings of the 2004 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB'04

作者： Ashlock, Daniel A. Emrich, Scott J. Bryden, Kenneth M. Corns, Steve M. Wen, Tsui-Jung Schnable, Patrick S. Mathematics Department Iowa State University Ames IA 50011 Bioinformatics Program Iowa State University Ames IA 50011 Mechanical Engineering Iowa State University Ames IA 5001 Agronomy Department Iowa State University Ames IA 50011

ISBN: (纸本)0780387287

This presents results on training both finite state classifiers and interpolated Markov models as classifiers for polymerase chain reaction primers. The goal of the study is to find techniques to decrease the number of primers that fail to amplify correctly within a large genomics project. Standard primer design packages already select primers in a manner consistent with current knowledge of the biophysics of DNA. The classifiers trained in this effort are used to capture lab and organism specific features of primer data and are used to post-process the output of standard primer design packages. The finite state classifiers in this study are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multi-population hybridization. This hybridization is akin to population seeding, not the more usual hybridization of evolutionary computation with other techniques. The interpolated Markov model is a form of Markov model that adapts to data rich and data sparse portions of the training set by using a variable order in its modeling. The interpolated Markov models exhibited slightly superior performance and trains with far higher speed. The finite state classifiers provide a substantially different classification, however, and require less training data. ©2004 ieee.

关键词： Enzymes

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A computational framework for nucleic acid sub-sequence identification

A computational framework for nucleic acid sub-sequence iden...

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4TH ieee symposium on bioinformatics and Bioengineering (BIBE 2004)

作者： Mann, S Chen, YPP Eaton, L Deakin Univ Sch Informat Technol Melbourne Vic Australia

ISBN: (纸本)0769521738

Identification of nucleic acid sub-sequences within larger background sequences is a fundamental need of the biology community. The applicability correlates to research studies looking for homologous regions, diagnostic purposes and many other related activities. This paper serves to detail the approaches taken leading to sub-sequence identification through the use of Hidden Markov Models and associated scoring optimisations. The investigation of techniques for locating conserved basal promoter elements correlates to promoter thus gene identification techniques. The case study centred on the TATA Box basal promoter element, as such the background is a gene sequence with the TATA Box the target. Outcomes from the research conducted, highlights generic algorithms for sub-sequence identification, as such these generic processes can be transposed to any case study where identification of a target sequence is required Paths extending from the work conducted in this investigation have led to the development of a generic framework for the future applicability of Hidden Markov Models to biological sequence analysis in a computational context.

关键词： Nucleic acids

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An effective approach for constructing the phylogenetic tree on a Grid-based architecture

An effective approach for constructing the phylogenetic tree...

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4TH ieee symposium on bioinformatics and Bioengineering (BIBE 2004)

作者： Liu, DSM Wu, CH Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan

ISBN: (纸本)0769521738

In biological research, scientists often need to use the information of the species to infer the evolutionary relationship among them. The evolutionary relationships are generally represented by a labeled binary tree, called the evolutionary tree (or phylogenetic tree). Reconstructing evolutionary tree is a major research problem in biology, and this problem is often known as phylogeny problem. The difficulty of such problem is that the number of possible evolutionary trees is very large. As the number of species increases, exhaustive enumeration of all possible relationships is not feasible. The quantitative nature of species relationships therefore requires the development of more rigorous methods for tree construction. The phylogeny problem is computationally intensive, thus it is suitable for distributed computing environment. Grid Computing (or computational Grid) is a new concept to integrate the CPU power the storage and other resources via Internet in order to get overall computing power Nowadays, many bioinformaticists are developing the BioGrid technology in order to solve the challenges that need intensive computing in biology. In this paper, we design and develop a Grid-based system, and propose an efficient method based on the concept of quartet for solving the phylogeny problem on this architecture.

关键词： bioinformatics Grid Computing Bio-Grid evolutionary tree divide-and-conquer approach quartet

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Cluster analysis of the p53 genetic regulatory network: topology and biology

Cluster analysis of the p53 genetic regulatory network: topo...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： J.S. Hallinan ARC Centre for Bioinformatics University of Queensland Australia

We describe a network module detection approach which combines a rapid and robust clustering algorithm with an objective measure of the coherence of the modules identified. The approach is applied to the network of genetic regulatory interactions surrounding the tumor suppressor gene p53. This algorithm identifies ten clusters in the p53 network, which are visually coherent and biologically plausible.

关键词： Genetics Network topology Cancer DNA Proteins Robustness Clustering algorithms Neoplasms bioinformatics Databases

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A memetic clustering algorithm for the functional partition of genes based on the gene ontology

A memetic clustering algorithm for the functional partition ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： N. Speer C. Spieth A. Zell Centre for Bioinformatics Tübingen (ZBIT) University of Tübingen Tubingen Germany

With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need of a functional grouping of genes arises. We propose a new clustering algorithm for the partition of genes or gene products according to their known biological function based on Gene Ontology terms. Ontologies offer a mechanism to capture knowledge in a shareable form that is also processable by computers. Our functional cluster algorithm promises to automatize, speed up and therefore improve biological data analysis.

关键词： Clustering algorithms Partitioning algorithms Ontologies Data analysis bioinformatics Throughput DNA Databases biology computing Genomics

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A multifuzzy filtering approach to reliable gene expression profile analysis

A multifuzzy filtering approach to reliable gene expression ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： H. Seker Centre for Computational Intelligence School of Computing De Montfort University Leicester UK

computational intelligent methods have become popular tools for gene expression profile analysis and for identifying a set of significant genes. However, their conclusions rely on an outcome of a single method. This work highlights the fact that each method can result in a different gene profile and set of the most significant genes. Therefore, the result cannot be accurate, and most importantly, not reliable. In order to draw a more reliable decision, a multifuzzy filtering approach to gene expression profile analysis is developed. The method is applied to the analysis of the yeast genes. The results suggest that the genes identified are more reliable and provide more valuable information than those stratified by a single method.

关键词： Filtering Gene expression Fungi DNA Information analysis Production Pattern analysis Pattern recognition Feature extraction Diseases

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Inferring neutral evolution from Parkinson's disease genes

Inferring neutral evolution from Parkinson's disease genes

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： G.W.L. Pang J.C. Rajapakse BioInfomtics Research Institute (BIRC) Nanyang Technological University Singapore Singapore

Comparative analysis of evolutionary rates of different species provides us with informative results as to how nucleotides mutate in species with different divergence times. Relative rate test is frequently deployed to test rates of synonymous and nonsynonymous substitutions. We are interested to use the test on a set of genes which are related to the Parkinson's Disease. Comparative genomics of human, chimp, mouse, chicken and fugu revealed most of the genes did not provide evidence of a rate heterogeneity, thereby inferring most of them are undergoing neutral or nearly neutral mutation. This allows us to get a better understanding of the evolutionary traits of genes implicated in Parkinson's Disease.

关键词： Parkinson's disease Evolution (biology) Testing Genomics bioinformatics Humans Genetic mutations Sequences DNA Neurons

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Analyzing gene expression data for childhood medulloblastoma survival with artificial neural networks

Analyzing gene expression data for childhood medulloblastoma...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： A. Narayanan E. Nana E. Keedwell University of Exeter Exeter UK Bioinformatics Laboratory University of Exeter Exeter UK School of Engineering Computer Science and Mathematics University of Exeter UK

One of the major problems facing gene expression researchers is how to reduce the high dimensionality of gene expression data in the face of small sample sizes in comparison to the number of genes measured. We report on the application of a single layer neural network for reducing the number of originally measured genes from over 7000 to 64 through repeated application of thresholds on the weights linking genes to the class values of samples. This results in a small but informative gene set for the domain of brain cancer that can be further analyzed through the application of symbolic machine learning techniques (See5) and cluster analysis (cluster and tree view).

关键词： Data analysis Gene expression Artificial neural networks Neoplasms Tumors Embryo Cancer Genetics Artificial intelligence Metastasis

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Graphical approach for motif recognition in DNA sequences

Graphical approach for motif recognition in DNA sequences

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： X. Yang J.C. Rajapakse School of Computer Engineering Bioinformatics Research Center Nanyang Technological University Singapore Singapore

Several algorithms have been developed for motif recognition in the past few years, superior in some sense over others, yet not a single one was declared to be the "best". Some of the well recognized algorithms are based on heuristic methods, such as Gibbs sampling and expectation maximization, and enumeration methods, such as Oligo-analysis. However, the inability to solve the "Challenge Problem" in motif recognition showed the drawbacks of the existing heuristic and enumeration methods. Two new algorithms were developed to resolve this problem but still, they suffered from time and space expense and the problem of local optima. We proposed a new algorithm which can solve the challenge problem with better performance even in very long sequences by applying dynamic programming for path searching in a graph and scanning with the consensus sequence to eliminate faked motif instances.

关键词： DNA Sequences bioinformatics Genomics Sampling methods Dynamic programming Senior members Character recognition Fungi Testing

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