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检索条件"任意字段=Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004"

共 371 条记录，以下是121-130 订阅

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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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Positive sample only learning (PSOL) for predicting RNA genes in E. coli

Positive sample only learning (PSOL) for predicting RNA gene...

引用

proceedings of the ieee computational systems bioinformatics conference, csb

作者： R.F. Meraz Xiaofeng He C.H.Q. Ding S.R. Holbrook Physical Biosciences Division Lawrence Berkeley National Laboratory Berkeley CA USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA USA

RNA genes lack most of the signals used for protein gene identification. A major shortcoming of previous discriminative methods to distinguish functional RNA (fRNA) genes from other non-coding genomic sequences is that only positive examples of fRNAs are known; there are no confirmed negatives - only intergenic sequences that may be positive or negative. To address this problem we developed the "positive sample only learning" (PSOL) method. This method can identify the most likely negative examples from an unlabeled set and is therefore able to distinguish putative functional RNA genes from other non-coding sequence. We compare RNA gene predictions using the PSOL method with previous large-scale analyses of the E. coli K12 genome.

关键词： RNA bioinformatics Genomics Iterative algorithms Helium Physics computing Laboratories Signal processing Proteins Large-scale systems

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A finite model theory for biological hypotheses

A finite model theory for biological hypotheses

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S. Racunas C. Griffin N. Shah Pennslyvania State University University Park PA USA

We have designed and implemented a set of software tools for the composition and evaluation of hypotheses about gene regulation in biological systems. Our software uses a unified formal grammar for the representation of both diagram-based and text-based hypotheses. The objective of this paper is to show how to use this grammar as the basis for an effective logic for specifying hypotheses about biological systems in precise model-theoretic terms. To accomplish this, we take inspiration from inflationary extensions to fixed point logics and define a new type of logic: a deflationary logic for describing the effects of experiments upon models of biological systems. We present results that characterize decidability, satisfiability, and inflationary/deflationary properties of this logic. We formally define what it means for a set of assertions to be discoverable under this new logic, and show that our software generates discoverable queries. Thus, we lay the groundwork for a formal treatment of machine-aided experimental design under the conceptual framework we have developed for our hypothesis evaluation software.

关键词： Biological system modeling Biological systems Biological processes Chromium Software tools Software systems Design for experiments Testing Logic design bioinformatics

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A novel computational framework for structural classification of proteins using local geometric parameter matching

A novel computational framework for structural classificatio...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S. Dua N. Kandiraju Data Mining Research Laboratory Louisiana Tech University USA

The objective of this study was to develop a novel and fast computational framework for classification of proteins using a series of secondary structure geometric parameter represented by an unexplored dihedral angle of a protein sequence. A dihedral angle is calculated between two planes represented by atomtuplets [N(i), C(i), N(i+1)] and [(C(i), N(i+1), C(ii+1)], of adjacent (i and i+1) amino acids of a protein structure. The comparison of two such series of dihedral angles, each representing a different protein structure, is based on subsequence matching which not only gives the extent of match but also provides with the approximate demographic information of the match which then is used in classification of proteins. The technique is tested over 25 proteins belonging to 5 different families randomly selected from Alpha, Beta, Alpha and Beta (alpha/beta) and multi-domain proteins (alpha and beta) classes. The classification rate is achieved with an accuracy of 88%.

关键词： Amino acids Protein engineering Data mining Laboratories Electronic mail Protein sequence Testing bioinformatics Indexing Demography

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PoPS: a computational tool for modeling and predicting protease specificity

PoPS: a computational tool for modeling and predicting prote...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S.E. Boyd M.G. de la Banda R.N. Pike J.C. Whisstock G.B. Rudy School of Computer Science & Software Engineering and Victorian Bioinformatics Consortium Monash University Melbourne VIC Australia Department of Biochemistry & Molecular Biology and Victorian Bioinformatics Consortium Monash University Melbourne VIC Australia Genetics & Bioinformatics Division Walter and Eliza Hall Institute of Medical Research Parksville VIC Australia

Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://***/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

关键词： computational modeling Predictive models Amino acids bioinformatics Bonding Sequences Proteins Biochemistry Biological control systems Diseases

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Multi-knockout genetic network analysis: the Rad6 example

Multi-knockout genetic network analysis: the Rad6 example

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： A. Kaufman M. Kupiec E. Ruppin Center of Neural Computation Hebrew University Jerusalem Israel School of Computer Science and School of Medicine Tel-Aviv University Tel-Aviv Israel

A novel and rigorous multi-perturbation shapley value analysis (MSA) method has been recently presented. The method addresses the challenge of defining and calculating the functional causal contributions of elements of a biological system. This work presents the first study applying MSA to the analysis of gene knockout data. The MSA identifies the importance of genes in the Rad6 DNA repair pathway of the yeast S. cerevisiae, quantifying their contributions and characterizing their functional interactions. Incorporating additional biological knowledge, a new functional description of the Rad6 pathway is provided, predicting the existence of additional DNA polymerase and RFC-like complexes. The MSA is the first method for rigorously analyzing multi-knockout experiments, which are likely to soon become a standard and necessary tool for analyzing complex biological systems.

关键词： Genetics DNA Biological systems System identification Fungi bioinformatics Organisms Biology computing Biotechnology Computer science

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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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In silico construction of the carbon fixation pathway in Synechococcus sp. WH8102

In silico construction of the carbon fixation pathway in Syn...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Phuongan Dam Zhengchang Su V. Olman Ying Xu Department of Biochemistry and Molecular Biology University of Georgia Athens USA Institute of Computational Biology Oak Ridge National Laboratory USA

Because the carbon fixation pathway plays an essential role in the primary production and natural carbon recycling process, and because the genome of the marine cyanobacterial Synechococcus sp. WH8102 (SYNWH8102) was recently sequenced, SYNWH8102 was chosen to further our understanding of the interaction and regulation of the carbon fixation pathway at the molecular level. In this abstract, we present the predicted carbon fixation pathway in SYNWH8102 as a result of our recently developed computational protocol for inference of regulatory and signaling pathways. The results of our pathway prediction include: (a) Major components of the carbon fixation pathway reported in the literature are present in SYNWH8102. (b) Approximately, 48 new candidates are added into the network from the results of the pathway expansion step. (c) Additionally, our in-house motif finding program, CUBIC, found several motifs that are present in the promoter regions of multiple genes involved in this pathway, suggesting that these genes are transcriptionally co-regulated.

关键词： Biochemistry bioinformatics Organisms Genomics Protocols Regulators Phylogeny computational biology Laboratories Data mining

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Exploring genomic context patterns for Rhodobacter sphaeroides in the HERBE knowledge discovery environment

Exploring genomic context patterns for Rhodobacter sphaeroid...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： H.J. Sofia A.L. Corrigan K.R. Klicker G. Chin E.G. Stephan Pacific Northwest National Laboratory Richland WA USA

Sophisticated information strategies are increasingly essential for biologists. We have built a powerful knowledge discovery resource using novel genomic context methods, interactive visualization strategies, and computational environment technologies. The Heuristic Entity Relationship Building Environment (HERBE) is a research platform for advanced database technologies that fuses data management solutions with knowledge management components to support the dynamic capture of concepts and observations as biologists explore large-scale data. The Similarity Box visualization software supports the ability of biologists to interact with large-scale computational results and evaluate relationships based on natural reasoning processes. We have applied these knowledge methods in the exploration of complex microbial genome relationships. We extracted a complete set of gene neighbor patterns for Rhodobacter sphaeroides using HERBE to map data structures for chromosomal contiguity against sequence similarity. We then organized these gene neighbor patterns by their phylogenetic profiles using Similarity Box to enable biologists to explore the results.

关键词： Genomics bioinformatics Data visualization Biology computing Knowledge management Large-scale systems Visual databases Fuses Environmental management Technology management

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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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