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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 879 条记录，以下是691-700 订阅

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Assigning gene ontology categories (GO) to yeast genes using text-based supervised learning methods

Assigning gene ontology categories (GO) to yeast genes using...

引用

Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： T. Izumitani H. Taira H. Kazawa E. Maeda NTT Communication Science Laboratories Kyoto Japan

We propose a method for assigning upper level gene ontology terms (GO categories) to genes using relevant documents. This method represents each gene as a vector using relevant documents to the gene. Then, binary classifiers are made for the GO categories using such supervised learning methods as support vector machines and maximum entropy method. We applied this method for assigning GO categories to yeast genes and achieved an average F-measure of 0.67, which is > 0.3 higher than the existing method developed by Raychaudhun et al. We also applied this method to genome-wide annotation for yeast by all GO Slim categories provided by SGD and achieved average F-measures of 0.58, 0.72, and 0.60, respectively, for the three GO parts: cellular component, molecular function, and biological process.

关键词： Ontologies Fungi Supervised learning Support vector machines bioinformatics Genomics Support vector machine classification Entropy Biological processes Databases

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A mixed factors model for dimension reduction and extraction of a group structure in gene expression data

A mixed factors model for dimension reduction and extraction...

引用

Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： R. Yoshida T. Higuchi S. Imoto Graduate University of Advanced Studies (SOKENDAI) Minato Tokyo Japan Institute for Statistical Mathematics Minato Tokyo Japan Human Genome Center Institute of Medical Science University of Tokyo Minato Tokyo Japan

When we cluster tissue samples on the basis of genes, the number of observations to be grouped is much smaller than the dimension of feature vector. In such a case, the applicability of conventional model-based clustering is limited since the high dimensionality of feature vector leads to overfilling during the density estimation process. To overcome such difficulty, we attempt a methodological extension of the factor analysis. Our approach enables us not only to prevent from the occurrence of overfilling, but also to handle the issues of clustering, data compression and extracting a set of genes to be relevant to explain the group structure. The potential usefulness are demonstrated with the application to the leukemia dataset.

关键词： Data mining Gene expression Principal component analysis bioinformatics Mathematics Humans Genomics Data compression Diseases Eigenvalues and eigenfunctions

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Whole genome sequencing, comparative genomics, and systems biology

Whole genome sequencing, comparative genomics, and systems b...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： E. Myers University of California Berkeley USA

The whole-genome shotgun sequencing method with paired end-reads has proven rapid and economical, producing high-quality reconstructions of Drosophila (2000), human (2001) and mouse (2001), in quick succession. We discuss the overall algorithmic strategy, the results one can expect by comparing the whole genome assembly of Drosophila against the recently finished sequence, and advances such as high-density solid state sequencing and single molecule detection systems. We anticipate having the euchromatic portions of the genomes of twelve species of Drosophila in the next year. We discuss the current state of the art in comparative gene finding, cis-control module finding, and possible improvements. The hope of these approaches is that we will be able to accurately identify the "parts lists" of the D. melanogaster genome, a basic prerequisite for systems biology. We conclude with a segment on the possibility of a program of high-throughput in-situ image analysis in Drosophila embryos. We describe what information we might collect and what we might be able to infer from it. It is our contention that this may be the best way to understand development from a systems perspective.

关键词： Genomics bioinformatics systems biology Image reconstruction Humans Mice Assembly systems Sequences Solid state circuits Image segmentation

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Recognition of exon/intron boundaries using dynamic ensembles

Recognition of exon/intron boundaries using dynamic ensemble...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Xuanfu Wu Zhengxin Chen Department of Computer Science University of Nebraska Omaha Omaha NE USA

ISBN: (纸本)0769521940

Many studies have been carried out in recognition of exon/intron boundaries. For example, PROCRUSTES uses similarity-based approach to gene recognition. Other examples include GRAIL (Gene Recognition and Assembly Internet Link) http://***/Grail-1.3/help/(1996) and Glimmer (Gene Locator and Interpolated Markov Modeler). Since the problem of recognition of exon/intron boundaries can be cast as a classification task, ensemble learning can be applied. An ensemble consists of a set of organized individual trained classifiers whose individual decisions are combined in a certain way for classification purpose. However, existing studies typically take static approaches which hampered flexibility for improved accuracy. To overcome this problem we have proposed the concept of dynamic ensemble and developed a new algorithm, BAGA, which combines bagging and genetic algorithm techniques.

关键词： Voting Genetic algorithms Biological cells Bagging Genetic mutations Computer science Assembly Internet Skeleton bioinformatics

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Comparative analysis of gene expression and DNA copy number data for pancreatic and breast cancers using an orthogonal decomposition

Comparative analysis of gene expression and DNA copy number ...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： J.A. Berger S. Hautaniemi S.K. Mitra Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Tampere International Center for Signal Processing Tampere University of Technology Tampere Finland

The causes of over-expression for many diseases are typically unknown, but current studies show that copy number aberrations may be strong candidates for driving gene overexpression. We present the use of the generalized singular value decomposition (GSVD) for simultaneously identifying relevant influences common to only copy numbers, gene expression, or both measurements in conjunction. These groups are reported and gene ontology (GO) annotations are used as a functional assessment of the groupings accompanied by probabilistic significance obtained by combinatorics. We illustrate this method for two independently published studies of pancreatic cancer and breast cancer, where public gene expression and DNA copy number data is provided and measured across numerous tumor cell lines.

关键词： Gene expression DNA Pancreas Breast cancer bioinformatics Singular value decomposition Genomics Cloning Signal processing Diseases

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MinPD: distance-based phylogenetic analysis and recombination detection of serially-sampled HIV quasispecies

MinPD: distance-based phylogenetic analysis and recombinatio...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： P. Buendia G. Narasimhan Bioinformatics Research Group School of Computer Science Florida International University Miami FL USA

A new computational method to study within-host viral evolution is explored to better understand the evolution and pathogenesis of viruses. Traditional phylogenetic tree methods are better suited to study relationships between contemporaneous species, which appear as leaves of a phylogenetic tree. However, viral sequences are often sampled serially from a single host. Consequently, data may be available at the leaves as well as the internal nodes of a phylogenetic tree. Recombination may further complicate the analysis. Such relationships are not easily expressed by traditional phylogenetic methods. We propose a new algorithm, called MinPD, based on minimum pairwise distances. Our algorithm uses multiple distance matrices and correlation rules to output a MinPD tree or network. We test our algorithm using extensive simulations and apply it to a set of HIV sequence data isolated from one patient over a period often years. The proposed visualization of the phylogenetic tree/spl bsol/network further enhances the benefits of our methods.

关键词： Human immunodeficiency virus Phylogeny bioinformatics Pathogens Immune system Sampling methods Capacitive sensors Diseases Computer science Electronic mail

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FastR: fast database search tool for non-coding RNA

FastR: fast database search tool for non-coding RNA

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： V. Bafna Shaojie Zhang Department of Computer Science and Engineering University of California San Diego La Jolla CA USA

The discovery of novel non-coding RNAs has been among the most exciting recent developments in biology. Yet, many more remain undiscovered. It has been hypothesized that there is in fact an abundance of functional non-coding RNA (ncRNA) with various catalytic and regulatory junctions. computational methods tailored specifically for ncRNA are being actively developed. As the inherent signal for ncRNA is weaker than that for protein coding genes, comparative methods offer the most promising approach, and are the subject of our research. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently compute all structural homologs (computed as a function of sequence and structural similarity) in a genomic database. Our approach, based on structural filters that eliminate a large portion of the database, while retaining the true homologs allows us to search a typical bacterial database in minutes on a standard PC, with high sensitivity and specificity. This is two orders of magnitude better than current available software for the problem.

关键词： Databases RNA Sequences Biology computing Proteins Genomics bioinformatics Filters Microorganisms Sensitivity and specificity

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High-throughput computational and experimental biology strategy in identifying tumor expressing CAMs

High-throughput computational and experimental biology strat...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： A. Sadanandam M.L. Varney R.K. Singh Department of Pathology and Microbiology University of Nebraska Medical Center Omaha NE USA

Organ-specific homing of tumor cells includes receptor-ligand interactions of cell adhesion molecules (CAMs), which stimulate or inhibit the cellular growth. In this report we identified unique CAMs using sequence and pattern analysis with organ-specific peptides as query against mouse genome and proteome sequences. The peptides were seven amino acids in length, and were affinity selected against specific organs utilizing in vivo phage display peptide library in NOD-SCID mice. Various databases were used for the analyses including Local Mouse Cell Adhesion Molecule (LMCAM) database developed by keyword search. Thirty annotated CAMs corresponding to eleven different organ-specific peptides were identified using the bioinformatics analysis. One such identified protein, SEMA5A is reported for the first time to be expressed in human pancreatic cancer cell lines. This combined strategy of experimental and computation biology is an initial approach in identifying novel tumor-specific molecules, thereby paving the way for complete understanding of their role in various processes of tumor metastasis and making organ-specific targeting possible using these peptides.

关键词： Biology computing computational biology Neoplasms Cams Peptides Sequences Metastasis bioinformatics Databases Adhesives

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A self-tuning method for one-chip SNP identification

A self-tuning method for one-chip SNP identification

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： M. Molla J. Shavlik T. Albert T. Richmond S. Smith University of Wisconsin Madison USA NimbleGen Systems Inc.

Current methods for interpreting oligonucleotide-based SNP-detection microarrays, SNP chips, are based on statistics and require extensive parameter tuning as well as extremely high-resolution images of the chip being processed. We present a method, based on a simple data-classification technique called nearest-neighbors that, on haploid organisms, produces results comparable to the published results of the leading statistical methods and requires very little in the way of parameter tuning. Furthermore, it can interpret SNP chips using lower-resolution scanners of the type more typically used in current microarray experiments. Along with our algorithm, we present the results of a SNP-detection experiment where, when independently applying this algorithm to six identical SARS SNP chips, we correctly identify all 24 SNPs in a particular strain of the SARS virus, with between 6 and 13 false positives across the six experiments.

关键词： Probes bioinformatics Organisms Genomics Tuning Statistical analysis Capacitive sensors DNA Statistics Databases

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Multiple alignment of rearranged genomes

Multiple alignment of rearranged genomes

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： A. Darling M. Craven B. Mau N.T. Perna Department of Computer Science University of Wisconsin Madison Madison WI USA Department of Animal Health and Biomedical Sciences University of Wisconsin Madison Madison WI USA Department of Biostatistics and Medical Informatics University of Wisconsin Madison Madison WI USA

The nature of large-scale evolutionary processes that shape genomes over time fundamentally differs from the forces governing local evolution within individual genes. Large-scale events such as horizontal transfer, genome rearrangements, gene duplication, and gene loss obscure the notion of orthology and have demanded new models of evolution. Multiple genome alignment tools must cope with such large-scale changes in addition to local changes such as nucleotide substitution and indels. Using simulated genomes containing both large and small-scale evolutionary changes, we present an alignment quality comparison of Mauve, a multiple genome aligner that considers large-scale evolutionary events, to alignments generated by other state-of-the-art genome alignment systems. Our results indicate that in the presence of large-scale rearrangement events, Mauve has superior accuracy. Mauve is available from http: //gel. ***/mauve.

关键词： Genomics bioinformatics Large-scale systems Sequences Organisms Computer science Animals Biomedical informatics Shape Discrete event simulation

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