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Non-occurring and rare quads in PDB and translated introns from XPro with possible applications in nanostructure design

Non-occurring and rare quads in PDB and translated introns f...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： G. Sampath J.T. Eyck Department of Computer Science Poughkeepsie NY USA

Exhaustive search over 17313 unique protein sequences in the database PDB indicates the absence of 4036 of the 160000 possible subsequences of four residues (quads). When the polypeptides obtained by translating 100000 prion sequences in the database XPro are searched the number drops to 424, which still exceeds what would be obtained by pure chance. More generally there are 11444 quads that occur 3 or fewer times in PDB. Using the Kyte-Doolittle hydrophobicity index, the 4036 quads (including the 424 absent in XPro) are divided into 16 groups, five of which can form unbroken helices or sheets by repetition. Most of the 16 groups are evenly distributed, one exception being quads with all-apolar residues, which are significantly less frequent. The helical and sheet structures so formed are artificial polypeptides not observed in nature. By using patterns from the other 11 groups more complex structures can be formed. Such structures could potentially serve as tubules and substrates in nanostructure design.

关键词： Proteins Databases Application software Computer science Educational institutions Frequency Nanotechnology Water bioinformatics conferences

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Clustering genes using gene expression and text literature data

Clustering genes using gene expression and text literature d...

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Proceedings of the ieee computational systems bioinformatics conference, csb

作者： C. Yang E. Zeng T. Li G. Narasimhan Bioinformatics Research Group (BioRG) School of Computer Science Florida International University Miami FL USA

Clustering of gene expression data is a standard technique used to identify closely related genes. In this paper, we develop a new clustering algorithm, MSC (Multi-Source Clustering), to perform exploratory analysis using two or more diverse sources of data. In particular, we investigate the problem of improving the clustering by integrating information obtained from gene expression data with knowledge extracted from biomedical text literature. In each iteration of algorithm MSC, an EM-type procedure is employed to bootstrap the model obtained from one data source by starting with the cluster assignments obtained in the previous iteration using the other data sources. Upon convergence, the two individual models are used to construct the final cluster assignment. We compare the results of algorithm MSC for two data sources with the results obtained when the clustering is applied on the two sources of data separately. We also compare it with that obtained using the feature level integration method that performs the clustering after simply concatenating the features obtained from the two data sources. We show that the z-scores of the clustering results from MSC are better than that from the other methods. To evaluate our clusters better, function enrichment results are presented using terms from the Gene Ontology database. Finally, by investigating the success of motif detection programs that use the clusters, we show that our approach integrating gene expression data and text data reveals clusters that are biologically more meaningful than those identified using gene expression data alone.

关键词： Gene expression Clustering algorithms Data mining bioinformatics Data analysis Databases Information analysis Computer science Performance analysis Algorithm design and analysis

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Classification method for prediction of multifactorial disease development using interaction between genetic and environmental factors

Classification method for prediction of multifactorial disea...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： Y. Tomita M. Yokota H. Honda Department of Biotechnology School of Engineering University of Nagoya Japan Department of Cardiovascular Genome Science School of Medicine University of Nagoya Japan

Multifactorial disease such as life style related diseases, for example, cancer, diabetes mellitus, myocardial infarction (Ml) and others, is thought to he caused by complex interactions between polygenic basis and various environmental factors. In this study, we used 22 polymorphisms on 16 candidate genes that have been characterized and potentially associated with MI in terms of biological function and 6 environmental factors. To predict development for MI and classify the subjects into personally optimum development patterns, we extracted risk factor candidates (RFCs) composed of state which is a derivative form of polymorphisms and environmental factors using statistical test and selected risk factors from RFCs using Criterion of Detecting Personal Group (CDPG) defined in this study. We could predict development of blinded data simulated as unknown their development more than 80% accuracy and identify their causal factors using CDPG.

关键词： Genetics Environmental factors Testing Cardiac disease Cardiovascular diseases Data mining bioinformatics Diabetes Performance analysis Performance evaluation

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Feature selection and classification of gene expression profile in hereditary breast cancer

Feature selection and classification of gene expression prof...

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Proceedings - HIS'04: 4th International conference on Hybrid Intelligent systems

作者： Raza, Mansoor Gondal, Iqbal Green, David Coppel, Ross L. GSCIT Faculty of IT Monash University Australia Deparment of Microbiology Monash University Australia Victorian Bioinformatics Consortium Monash University Australia

ISBN: (纸本)0769522912

Correct classification and prediction of tumor cells is essential for successful diagnosis and reliable future treatment. However, it is very challenging to distinguish between tumor classes using microarray with thousands of gene expressions. Removing irrelevant genes is very helpful for us to learn the relationship between genes and tumors. In this paper we have used two methods: Multivariate Permutation Test (MPT) and Significant Analysis of Microarray (SAM) to select significant genes for feature selection. Using those selected features, we applied Support Vector Machine, (SVM) with polynomial, radial and linear kernels, to predict the class of testing data. Our result shows that all the samples are classified correctly. We have achieved 100% accuracy in classification among all the samples with polynomial kernel of SVM while Liner kernel shows no misclassification among BRCA1-BRCA2 and BRCA1-sporadic. © 2005 ieee.

关键词： Cells

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An approach to generating and verifying complex scripts and procedures

An approach to generating and verifying complex scripts and ...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： J.L. Rash M.G. Hinchey D. Gracanin C.A. Rouff Information Systems Division NASA Goddard Space Flight Center Greenbelt MD USA Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA Advanced Concepts Business Unit SAIC McLean VA USA

Currently available tools and methods for system development that start with a formal model of a system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The "gap" that such tools and methods leave unfilled is that the formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the classes of complex systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements expressed in restricted natural language, or appropriate graphical notations, into a provably equivalent formal model that can be used as the basis for code generation and other transformations. The same approach may be applied to address computer science aspects of bioinformatics problems. Many software tools for bioinformatics have been developed using scripting languages such as Perl and Python. Scripts are developed based on a set of requirements that can be expressed using English-like statements. Using our approach, these may be used to automatically generate and validate scripts rather than write them from scratch.

关键词： bioinformatics Software tools Computer science Databases Software performance Hardware Software quality NASA Information systems Natural languages

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Image data mining of fruitfly gene expression patterns

Image data mining of fruitfly gene expression patterns

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： H. Peng Genomics Division Lawrence Berkeley National Laboratory University of California Berkeley CA USA

Understanding the spatio-temporal gene expression patterns is critical in studying genes roles and their complicated relationships. With the availability of high-resolution digital images of in situ mRNA hybridization expression pattern of Drosophila embryos collected through both the Berkeley Drosophila Transcriptional Network Project (BDTNP) and the Berkeley Drosophila Genome Project (BDGP), we will be able to answer a series of interesting questions, e.g., what are the variations of gene expression patterns, what are the patterning process of gene expression, etc. To achieve these goals at a large scale, it is extremely important to automate the image mining and informatics processes of embryogenesis expression patterns. We developed computer programs to register, compare and analyze these spatio-temporal pattern images. In this talk, I will review four projects on image mining of fruitfly embryo gene expression patterns: (a) Gaussian mixture model based embryonic expression pattern extraction and comparison, (b) Gene expression pattern clustering using novel MSTCUT and probabilistic ensemble clustering techniques, (c) Surface/volume models for 3D modeling and registration of early embryos, and (d) A manifold learning method for spatio-temporal registration of 3D gene expression patterns and reconstruction of the embryonic developmental time series.

关键词： Data mining Gene expression Embryo Digital images Genomics bioinformatics Large-scale systems Informatics Registers Image analysis

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Chemical structure-activity relationship visualization using structure-activity maps

Chemical structure-activity relationship visualization using...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： W. Fan X. Lin Y.-W. Hsieh B. Lin J.W. Baker C. Tsai Department of Computer Science Kent University Kent OH USA Department of Chemistry Kent University Kent OH USA

This study shows an effective way to explore and visualize the structure-activity relationships (SARs) of flavonoids with antioxidant activity using structure- activity maps (SAMs). SAMs are graphical maps plotting molecular descriptors such as NAB or MSI against their biological activities. NAB (number of non-hydrogen atoms and bonds in a molecule) or MSI (molecular similarity index) is used to quantify the chemical structures. SA Ms pro vide a very efficient method for representing and visualizing SAR information in a biochemical database. SAMs also provide a simple and effective way for ordering and grouping compounds. SA Ms were examined for compounds grouped according to NAB to determine important activity trends utilizing structural orderings. SA Ms were used to systematically identify the effects of chemical modification on the activities of compounds and to determine the site and type of modifications for improved activity.

关键词： Visual databases Chemistry Computer science Chemical compounds Biological materials Biomedical materials Data visualization Atomic measurements Biology computing bioinformatics

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Registering Drosophila embryos at cellular resolution to build a quantitative 3D atlas of gene expression patterns and morphology

Registering Drosophila embryos at cellular resolution to bui...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： C.C. Fowlkes C.L. Luengo Hendriks S.V.E. Keranen M.D. Biggin D.W. Knowles D. Sudar J. Malik Department of Electrical Engineering and Computer Science University of California Berkeley USA Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory USA

The Berkeley Drosophila Transcription Network Project is developing a suite of methods to convert volumetric data generated by confocal fluorescence microscopy into numerical three dimensional representations of gene expression at cellular resolution. One key difficulty is that fluorescence microscopy can only capture expression levels for a few gene products in a given animal. We report on a method for registering 3D expression data from different Drosophila embryos stained for overlapping subsets of gene products in order to build a composite atlas, ultimately containing co-expression information for thousands of genes. Our techniques have also allowed the discovery of a complex pattern of cell density across the blastula that changes over time and may play a role in gastrulation.

关键词： Embryo Gene expression Morphology bioinformatics Genomics Fluorescence Microscopy Animals Cellular networks Regulators

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Effective constrained dynamic simulation using implicit constraint enforcement

Effective constrained dynamic simulation using implicit cons...

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2005 ieee International conference on Robotics and Automation

作者： Hong, Min Choi, Min-Hyung Jung, Sunhwa Welch, Samuel Trapp, John Bioinformatics Health Sciences Center University of Colorado at Denver 4200 E. 9th Avenue Denver CO 80262 United States Computer Science and Engineering Campus Box 104 P. O. Box 173364 Denver CO 80217 United States Mechanical Engineering Campus Box 104 P. O. Box 173364 Denver CO 80217 United States

ISBN: (纸本)078038914X

Stable and effective enforcement of hard constraints is one of the crucial components in controlling physics-based dynamic simulation systems. The conventional explicit Baumgarte constraint stabilization confines the time step to be within a stability limit and requires users to pick problem-dependent coefficients to achieve fast convergence or to prevent oscillations. The recently proposed post-stabilization method has shown a successful constraint drift reduction but it does not guarantee the physically correct behavior of motion and requires additional computational cost to decrease the constraint errors. This paper presents our new implicit constraint enforcement technique that is stable over large time steps and does not require problem dependent stabilization parameters. This new implicit constraint enforcement method uses the future time step to estimate the correct magnitude of the constraint forces, resulting in better stability over bigger time steps. More importantly, the proposed method generates physically conforming constraint forces while minimizing the constraint drifts, resulting in physically correct motion. Its asymptotic computational complexity is same as the explicit Baumgarte method. It can be easily integrated into various constrained dynamic systems including rigid body or deformable structure applications. This paper describes a formulation of implicit constraint enforcement and an accumulated constraint error and dynamic behavior analysis for comparison with existing methods. © 2005 ieee.

关键词： Constraint theory

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bioinformatics approach for exploring MS/MS proteomics data

Bioinformatics approach for exploring MS/MS proteomics data

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38th Annual Hawaii International conference on System Sciences, HICSS 2005

作者： Singhal, Mudita Klicker, Kyle Chin, George Trease, Lynn Stephan, Eric Gracio, Deborah

The use of computer tools and technologies is unavoidable when it comes to conducting mass spectrometry (MS) research at any significant level. This is mainly due to the large volume of MS data and the processing rates required. Most of the existing tools focus on one particular task: be it storing and maintaining the data or visualizing the dataset to draw inferences from the data. But for the researcher the problem of manually retrieving a large dataset from a datasource and customizing it for a particular visualization application is a daunting task in itself. This paper describes the computational Cell Environment (CCE) which is a problem-solving environment for systems biology that provides uniform and integrated access to distributed, heterogeneous biological data sources and analysis applications, through a multi-tiered architecture. This paper also illustrates the necessity for such a tool by discussing its usage in proteomics research being performed on the diseased and normal states of Deinococcus Radiodurans along with corresponding curated data collected from community resources.

关键词： Database systems

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