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Education for digital librarianship: Employers' needs and how they can be addressed

Proceedings of the Association for Information science and Technology 2007年第1期43卷 1-7页

作者： June Abbas Kyung-Sun Kim Barbara M. Wildemuth Youngok Choi Javed Mostafa Kristine Brancolini Jeffrey Pomerantz Abby Clobridge (Organizer and Moderator) School of Informatics State University of New York at Buffalo (Organizer) School of Library and Information Studies University of Wisconsin-Madison School of Information and Library Science University of North Carolina at Chapel Hill Dept. of Computer Science State University of New York at Oswego School of Library and Information Science Indiana University Digital Library Program Indiana University School of Information and Library Science University of North Carolina Information Services & Resources Bucknell University

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Statistical Dependence in Biological Sequences

Statistical Dependence in Biological Sequences

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IEEE International Symposium on Information Theory

作者： Hasan Metin Aktulga Ioannis Kontoyiannis L. Alex Lyznik Lukasz Szpankowski Ananth Y. Grama Wojciech Szpankowski Department of Computer Scienc Purdue University West Lafayette IN U.S.A. Department of Informatics Athens University of Econ Business Athens Greece Department of Informatics Athens Univ of Econ & Business Athens Greece Pioneer Hi-Bred International Johnston IA USA Bioinformatics Program UCSD San Diego CA U.S.A. Department of Computer Science Purdue University West Lafayette IN U.S.A.

ISBN: (纸本)9781424413973;1424413974

We demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural ependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from CODIS, we demonstrate that our approach is well suited for the problem of discovering short tandem repeats (STRs).

关键词： RNA Mutual information Sequences DNA Splicing Protein engineering Molecular biophysics computer science Bioinformatics Information analysis

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AlzPharm: Integration of neurodegeneration data using RDF

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BMC Bioinformatics 2007年第SUPPL. 3期8卷 S4-S4页

作者： Lam, Hugo Y.K. Marenco, Luis Clark, Tim Gao, Yong Kinoshita, June Shepherd, Gordon Miller, Perry Wu, Elizabeth Wong, Gwendolyn T. Liu, Nian Crasto, Chiquito Morse, Thomas Stephens, Susie Cheung, Kei-Hoi Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Anesthesiology Yale University New Haven CT United States Department of Neurobiology Yale University New Haven CT United States Department of Molecular Cellular and Developmental Biology Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Initiative in Innovative Computing Harvard University Cambridge MA United States Massachusetts General Hospital Boston MA United States Alzheimer Research Forum Burlington MA United States Oracle Burlington MA United States

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results su

关键词： Data integration

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An Information Theoretic Framework for Ontology-based Bioinformatics

An Information Theoretic Framework for Ontology-based Bioinf...

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Information Theory and Applications Workshop, ITA

作者： Gil Alterovitz Michael Xiang Marco F. Ramoni Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Division of Health Sciences and Technology Harvard Medical School and Massachusetts Institute of Technology Boston MA Children's Hospital Informatics Program Boston MA Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA.

With myriad information being generated from high-throughput experiments such as microarrays and sequencing technologies, an ever-increasing amount of data is being recorded and analyzed with the help of hierarchical ontologies, such as the gene ontology (GO). We have developed a novel framework- based on the well established foundations of information theory- that allows for the evaluation of new types of hypotheses. The framework, encapsulated in open biomedical ontology-based exploration and search (OBOES), has already been applied in the investigation of different kinds of questions. The resulting framework enables the new field of information theoretic ontology-based analysis. We have applied this framework to create methods to re-engineer ontologies, explore fundamental questions on the evolution of biological complexity, determine optimal ontology terms for bioinformatics analysis, and quantify the usefulness of biofluids as proxies for tissues/diseases. In each case, we found that our methods provide novel, significant findings. An open source Java implementation of OBOES is available at: http://***.

关键词： Ontologies Bioinformatics Genomics Information analysis Biological information theory Information theory Genetic communication Humans Biochemistry Frequency

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Evolving neural networks for pharmaceutical research

Evolving neural networks for pharmaceutical research

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2006 International Conference on Hybrid Information Technology, ICHIT 2006

作者： Grosan, Crina Abraham, Ajith Tigan, Stefan Tae, Gyu Chang Dong, Hwan Kim Department of Computer Science Babes-Bolyai University Cluj-Napoca 3400 Romania IITA Professorship Program School of Computer Science and Engineering Chung-Ang University Seoul 156-756 Korea Republic of University Iuliu Haţieganu Faculty of Medicine Department of Biostatistics and Medical Informatics Cluj-Napoca Romania

ISBN: (纸本)0769526748

This article presents the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose an evolutionary artificial neural network and the performance is compared with a neuro-fuzzy system and an artificial neural network trained using scaled conjugate gradient algorithm. This research used the experimental data obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data and the number of experimental data is reduced due to the costs and time durations of experimentations. We obtain in this way a better estimation of some drug parameters. Experiment results indicate that the proposed method is efficient. © 2006 IEEE.

关键词： Neural networks

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Evolving Neural Networks for Pharmaceutical Research

Evolving Neural Networks for Pharmaceutical Research

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International Conference on Hybrid Information Technology, ICHIT

作者： Crina Grosan Ajith Abraham Stefan Tigan Tae Gyu Chang Dong Hwan Kim Department of Computer Science Babes Bolyai University Cluj-Napoca Romania IITA Professorship Program School of Computer Science and Engineering Chung-Ang University Seoul South Korea Faculty of Medicine Department of Biostatistics and Medical Informatics University Iuliu Hatieganu Cluj-Napoca Romania

This article presents the application of meta-learning evolutionary artificial neural network (MLEANN) for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose an evolutionary artificial neural network and the performance is compared with a neuro-fuzzy system and an artificial neural network trained using scaled conjugate gradient algorithm. This research used the experimental data obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data and the number of experimental data is reduced due to the costs and time durations of experimentations. We obtain in this way a better estimation of some drug parameters. Experiment results indicate that the proposed method is efficient

关键词： Neural networks Pharmaceuticals Drugs Artificial neural networks computer science Costs Biomedical engineering Biomedical informatics Application software Fuzzy neural networks

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Biological network epitomes via topological compression

Biological network epitomes via topological compression

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Gil Alterovitz Marco F. Ramon Health Science and Technology/Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology / Harvard Medical School- Partners Center for Genetics and Genomics Boston 02115 USA.

High-throughput generation of new types of relational biological datasets is creating a demand for network-based signal processing and pattern recognition to provide new insights. Such networks are often too large to interpret visually and too complicated to be explained solely based on local topological properties. Just as signal processing and statistical techniques have been used in traditional, sequential-based biological datasets, so too are methodologies needed to automatically discern patterns in the huge, emerging networks. One way to do this is by transforming these very large networks into discernable epitomes, or abstracts, of the original networks. This work presents an approach for doing this via topological compression. Through capturing nodes' global topologies and subsequent compression, a new network epitome can be derived. Here, this is done with an E. Coli gene regulation network, resulting in biological findings that could not be derived from the local topology of the original network.

关键词： Biological information theory Network topology Genomics Bioinformatics Biomedical signal processing Compression algorithms Visualization Biology Gas insulated transmission lines computer science

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Gene lethality detection across biological network domains: Hubs versus stochastic global topological analysis

Gene lethality detection across biological network domains: ...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Gil Alterovitz Vinayak Muralidhar Marco F. Ramoni Health Science and Technology/Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA Harvard Medical School-Partners Center for Genetics and Genomics Boston USA Harvard-MIT Division of Health Sciences and Technology Children's Hospital Informatics Program Boston USA

In this paper, we investigate the properties of lethal genes in E. coli, our model organism. Topological analysis of networks of functional interactions among genes has shown that lethal genes share common local connectivity properties. In this paper, we analyze cellular networks across three domains. We show that a stochastic global topological analysis, via random walks, is more effective at predicting gene lethality than simply looking at local topology using the standard hub-based method. We also introduce the possibility of using metabolic pathways to understand lethal genes, as regulating these pathways is among one of the most important functions of the gene-encoded proteins. Additionally, we analyze lethal genes in terms of the Gene Ontology (GO) and find that the graph forms two highly connected clusters that are each GO enriched for specific terms. We also find that lethal metabolic regulators are extremely enriched. Finally, we provide applications of the work and avenues for future research.

关键词： Stochastic processes Proteins Organisms Humans Genomics Bioinformatics Databases Genetics Network topology Production

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Automatic Medical Image Annotation and Retrieval using SEMI-SECC

Automatic Medical Image Annotation and Retrieval using SEMI-...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jian Yao Zhongfei Zhang Sameer Antani Rodney Long George Thoma Department of Computer Science State University of New York at Binghamton Binghamton USA Medical Informatics Training Program of the Lister Hill National Center for Biomedical Communications National Library of Medicine Department of Computer Science State University of New York at Binghamton Binghamton National Library of Medicine National Institutes of Health Bethesda MD 20894 National Library of Medicine National Institutes of Health Bethesda

The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image retrieval method based on SEMI-supervised semantic error-correcting output codes (SEMI-SECC). The experimental results on IMAGECLEF 2005 annotation data set clearly show the strength and the promise of the presented methods

关键词： Biomedical imaging Image retrieval Medical diagnostic imaging Information retrieval Image databases Elbow Libraries Foot computer science computer errors

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Incomplete inverse preconditioners

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ARI Bulletin of the Istanbul Technical University 2005年第3期54卷 48-64页

作者： Alvarado, Fernando Dag, Hasan Department of Electrical and Computer Engineering University of Wisconsin Madison 53706 United States Computational Science and Engineering Program Informatics Institute Istanbul Technical University 34469 Maslak Istanbul Turkey

Incomplete LU factorization is a valuable preconditioning approach for sparse iterative solvers. An "ideal" but inefficient preconditioner for the iterative solution of Ax = b is A-1 itself. This paper describes a preconditioner based on sparse approximations to partitioned representations of A-1, in addition to the results of implementation of the proposed method in a shared memory parallel environment. The partitioned inverses are normally somewhat sparse. Their sparsity can be enhanced with suitable ordering and partitioning algorithms. Sparse approximations to these partitioned inverse representations can be obtained either by discarding selected nonzero entries of these inverses or by precluding the creation of some inversion fills. Experimental results indicate that the use of these partitioned incomplete inverses as preconditioners results in excellent highly parallel preconditioners.

关键词： Conjugate gradient Iterative methods Parallel computation Partitioned inverse method Preconditioner

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