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Noise propagation in transcriptional cascades

Noise propagation in transcriptional cascades

引用

ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： S. Hooshangi S. Thiberge R. Weis Siberian State Geodetic Academy Novosibirsk Russia Novosibirsk State Technical University Novosibirsk Russia

The flow of information through a biological network can greatly influence the operation and behavior of the system. The synthetic transcriptional cascades of various lengths and study of their dynamic and steady state behavior both experimentally and through a stochastic model are presented. These systems are used to analyze sensitivity and noise propagation as a function of synthetic network complexity. The steady state switching behavior that becomes sharper with longer cascades is demonstrated experimentally. The experimental results demonstrating the network properties correlate well with the simulated model.

关键词： Steady-state Intelligent networks Stochastic resonance Attenuation Filters computational modeling bioinformatics conferences

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On optimizing distance-based similarity search for biological databases

On optimizing distance-based similarity search for biologica...

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： R. Mao W. Xu S. Ramakrishnan G. Nuckolls D.P. Miranker Department of Computer Sciences Center for Computational Biology and Bioinformatics University of Texas Austin Austin TX USA

Similarity search leveraging distance-based index structures is increasingly being used for both multimedia and biological database applications. We consider distance-based indexing for three important biological data types, protein k-mers with the metric PAM model, DNA k-mers with Hamming distance and peptide fragmentation spectra with a pseudo-metric derived from cosine distance. To date, the primary driver of this research has been multimedia applications, where similarity functions are often Euclidean norms on high dimensional feature vectors. We develop results showing that the character of these biological workloads is different from multimedia workloads. In particular, they are not intrinsically very high dimensional, and deserving different optimization heuristics. Based on MVP-trees, we develop a pivot selection heuristic seeking centers and show it outperforms the most widely used corner seeking heuristic. Similarly, we develop a data partitioning approach sensitive to the actual data distribution in lieu of median splits.

关键词： Indexing Multimedia databases bioinformatics Indexes Biological system modeling Hamming distance Peptides Data structures Information retrieval Biology computing

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Peptide charge state determination for low-resolution tandem mass spectra

Peptide charge state determination for low-resolution tandem...

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： A.A. Klammer C.C. Wu M.J. MacCoss W.S. Noble Department of Genome Sciences Seattle WA USA Department of Pharmacology UCHSC Aurora CO USA

Mass spectrometry is a particularly useful technology for the rapid and robust identification of peptides and proteins in complex mixtures. Peptide sequences can be identified by correlating their observed tandem mass spectra (MS/MS) with theoretical spectra of peptides from a sequence database. Unfortunately, to perform this search the charge of the peptide must be known, and current charge-state-determination algorithms only discriminate singly-from multiply-charged spectra: distinguishing +2 from +3, for example, is unreliable. Thus, search software is forced to search multiply-charged spectra multiple times. To minimize this inefficiency, we present a support vector machine (SVM) that quickly and reliably classifies multiply-charged spectra as having either a +2 or +3 precursor peptide ion. By classifying multiply-charged spectra, we obtain a 40% reduction in search time while maintaining an average of 99% of peptide and 99% of protein identifications originally obtained from these spectra.

关键词： Peptides Mass spectroscopy Sequences Proteins bioinformatics Support vector machines Genomics Databases Support vector machine classification Search methods

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Choosing SNPs using feature selection

Choosing SNPs using feature selection

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： T.M. Phuong Z. Lin R.B. Altman Department of Information Technology Posts and Telecommunications Institute of Technology Hanoi Vietnam Department of Genetics University of Stanford Stanford CA USA

A major challenge for genomewide disease association studies is the high cost of genotyping large number of single nucleotide polymorphisms (SNP). The correlations between SNPs, however, make it possible to select a parsimonious set of informative SNPst known as "tagging" SNPs, able to capture most variation in a population. Considerable research interest has recently focused on the development of methods for finding such SNPs. In this paper, we present an efficient method for finding tagging SNPs. The method does not involve computation-intensive search for SNP subsets but discards redundant SNPs using a feature selection algorithm. In contrast to most existing methods, the method presented here does not limit itself to using only correlations between SNPs in local groups. By using correlations that occur across different chromosomal regions, the method can reduce the number of globally redundant SNPs. Experimental results show that the number of tagging SNPs selected by our method is smaller than by using block-based methods.

关键词： Tagging bioinformatics Genomics Biological cells Principal component analysis Genetics Diseases Humans Size measurement Information technology

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Investigation into biomedical literature classification using support vector machines

Investigation into biomedical literature classification usin...

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： N. Polavarapu S.B. Navathe R. Ramnarayanan A. ul Haque S. Sahay Y. Liu School of Biology Georgia Institute of Technology Atlanta GA USA College of Computing Georgia Institute of Technology Atlanta GA USA

Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typically formulates boolean queries followed by scanning the retrieved records for relevance, which is very time consuming and error prone. We applied Support Vector Machines (SVM) for automatic retrieval of PubMed articles related to Human genome epidemiological research at CDC (Center for disease Control and Prevention). In this paper, we discuss various investigations into biomedical literature classification and analyze the effect of various issues related to the choice of keywords, training sets, kernel functions and parameters for the SVM technique. We report on the various factors above to show that SVM is a viable technique for automatic classification of biomedical literature into topics of interest such as epidemiology, cancer, birth defects etc. In all our experiments, we achieved high values of PPV, sensitivity and specificity.

关键词： Support vector machines Support vector machine classification Databases Information resources Humans Genomics bioinformatics Diseases Automatic control Kernel

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Automated validation of polymerase chain reactions using amplicon melting curves

Automated validation of polymerase chain reactions using amp...

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： T.P. Mann R. Humbert J.A. Stamatoyannopolous W.S. Noble Department of Genome Sciences University of Washington Seattle WA USA Department of Molecular Biology Regulome Seattle WA USA

PCR, the polymerase chain reaction, is a fundamental tool of molecular biology. Quantitative PCR is the gold-standard methodology for determination of DNA copy numbers, quantitating transcription, and numerous other applications. A major barrier to large-scale application of PCR for quantitative genomic analyses is the current requirement for manual validation of individual PCR reactions to ensure generation of a single product. This typically requires visual inspection either of gel electrophoreses or temperature dissociation ("melting") curves of individual PCR reactions-a time-consuming and costly process. Here we describe a robust computational solution to this fundamental problem. Using a training set of 10,080 reactions comprising multiple quantitative PCR reactions from each of 1,728 unique human genomic amplicons, we developed a support vector machine classifier capable of discriminating single-product PCR reactions with better than 99% accuracy. This approach has broad utility, and eliminates a major bottleneck to widespread application of PCR for high-throughput genomic applications.

关键词： Polymers Genomics bioinformatics DNA Large-scale systems Inspection Temperature Robustness Humans Support vector machines

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Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching

Estimating time-dependent gene networks from time series mic...

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： R. Yoshida S. Imoto T. Higuchi Institute for Statistical Mathematics Minato Tokyo Japan Human Genome Center Institute of Medical Science University of Tokyo Minato Tokyo Japan

In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

关键词： Bayesian methods Vectors Mathematical model Differential equations Gene expression Mathematics bioinformatics Time series analysis State estimation Statistical analysis

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Analysis of SNP-expression association matrices

Analysis of SNP-expression association matrices

引用

Proceedings of the ieee computational systems bioinformatics conference, csb

作者： A. Tsalenko R. Sharan H. Edvardsen V. Kristensen A.-L. Boerresen-Dale A. Ben-Dor Z. Yakhini Agilent Technologies Palo Alto CA USA School of Computer Science Tel-Aviv University Tel-Aviv Israel Department of Genetics University of Oslo Oslo Norway

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 49 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

关键词： Genetics Gene expression bioinformatics Mice Genomics Throughput Regulators Diseases Proteins Modulation coding

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Autonomicity - an antidote for complexity?

Autonomicity - an antidote for complexity?

引用

ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： R. Sterritt M. Hinchey School of Computing and Mathematics Faculty of Engineering University of Ulster UK Software Engineering Laboratory NASA Goddard Space Flight Center Greenbelt MD USA

Autonomic computing and other self-managing system initiatives, many strongly based on biological metaphors, are emerging as a significant new vision for the design and development of complex computer systems. They offer the promise of controlling complexity through the achievement of self governance (autonomy) and self management (autonomicity). We consider how complexity is exhibited in the computer industry as a whole, and how the situation is deteriorating, rather than improving. We consider how autonomous and autonomic systems, with their biological inspiration, can provide a framework for tackling complexity and overcoming the problems of its (unavoidable) inherent existence in certain classes of systems.

关键词： Biology computing Computer vision bioinformatics Hardware Software maintenance Software performance Design engineering Computer industry Pervasive computing computational biology

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Towards interactive exploration of images, meta-data, and analytic results in the open microscopy environment

Towards interactive exploration of images, meta-data, and an...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： H. Hochheiser I.G. Goldberg Image Informatics and Computational Biology Unit Laboratory of Genetics National Institute on Aging National Institutes of Health Baltimore MD USA

The collection, management, and analysis of microscopy data present numerous informatics challenges. Appropriate user tools are needed to support acquisition, creation, analysis, annotation and interpretation of microscopy data. The Open Microscopy Environment (OME) is a database-driven system for the storage, analysis, management, and interpretation of microscopy data. An examination of these tasks in the context of regularly occurring structures in OME's data model motivates a framework for constructing these tools. Initial implementations of several components, linked together in a common environment, provide a partial realization of this framework.

关键词： Image analysis Microscopy Data models Environmental management Data analysis Biomedical informatics Image storage Image databases bioinformatics computational biology

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