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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Long Cheng Yue Ma Department of Computer Science Technische Universitat Dresden Germany LRI - CNRS & Univcrsite Paris Sud Orsay France

ISBN: (纸本)9781479975037

Heterogeneous data resources in biomedicine become available both in structured and unstructured formats, such as scientific publications, healthcare guidelines, controlled vocabularies, and formal ontologies. %Increasing researches have been conducted to Bridging the gaps among these heterogeneous data is useful to discovery implicit knowledge. To make this happen, efficient computational approaches are a necessity for applications in such a knowledge- and data-intensive domain. In this paper, we first define a particular task, relation alignment, which is to identify textual evidences for biomedical ontologies. Then, we investigate two parallel approaches for this task over distributed systems and present the details of their implementations. Moreover, we characterize the performance of our methods through extensive experiments, thereby allowing researchers to make a more informed choice in the presence of large-scale biomedical data.

关键词： Ontologies Runtime Distributed databases bioinformatics Semantics Scalability Medical services

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Load analysis and cost estimation of parallel constrained producer-consumer algorithms

Load analysis and cost estimation of parallel constrained pr...

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ieee International conference on Cluster Computing

作者： Tariq Kamal Keith R. Bisset Ali R. Butt Madhav V. Marathe Virginia Bioinformatics Institute Virginia Tech Blacksburg VA USA Department of Computer Science Virginia Tech Blacksburg VA USA

Cost estimation is crucial for optimizing the performance of parallel algorithms on distributed systems. This paper performs load analysis and cost estimation of constrained producer-consumer (CPC) parallel algorithms. In CPC algorithms, the computation is performed by classes of nodes (tasks), separated in time. For any given CPC problem, the cost can be modelled as a linear combination of computational imbalance components and a communication load. These components can be determined from analysis of the inter-task communication of the algorithm.

关键词： computational modeling Load modeling Algorithm design and analysis Estimation Synchronization Mathematical model

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ENISI MSM: A novel multi-scale modeling platform for computational immunology

ENISI MSM: A novel multi-scale modeling platform for computa...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yongguo Mei Adria Carbo Raquel Hontecillas Stefan Hoops Nathan Liles Pinyi Lu Casandra Philipson Josep Bassaganya-Riera The Center for Modeling Immunity to Enteric Pathogens Nutritional Immunology and Molecular Medicine Laboratory Virginia Bioinformatics Institute Blacksburg VA USA

Biological systems span several orders of magnitude in space and time from intracellular pathways to tissue-level processes. Many studies focus on molecular level events while other studies focus on cellular level and tissue level interactions. The immune system is highly complex and dynamic, encompassing hierarchical interactions with dimensions ranging from nanometers to meters and time scales from nanoseconds to years. To comprehensively model immunological processes computationally, multi-scale models are needed. However, the lack of multi-scale modeling tools can be a deterrent to advance the understanding of the immune system across scales. In this paper, we developed an object-oriented multi-scale modeling (MSM) platform, ENISI MSM, that integrates agent-based modeling (ABM), ordinary-differential equations (ODE), and partial differential equations (PDE) models. To our best knowledge, this is the first such multi-scale modeling platform that is capable of integrating ODE, PDE, and ABM models together. The tool is developed in Java and is object-oriented. For example, the agents are objects and the ODE and PDE solvers are also objects. ENISI MSM also provides user-friendly interfaces and visualizations. We developed a multi-scale CD4+ T cell differentiation model in the context of gut inflammatory and showed the effectiveness of ENISI MSM.

关键词： Mathematical model computational modeling Biological system modeling Computer architecture Object oriented modeling Microprocessors Visualization

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Comparison and Evaluation of Signal Representations for a Carbon Nanotube computational Device

Comparison and Evaluation of Signal Representations for a Ca...

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ieee International conference on Evolvable systems

作者： Odd Rune Lykkebo Gunnar Tufte The Norwegian University of Science and Technology Department of Computer and Information Science

ISBN: (纸本)9781479944781

Evolution in Materio (EIM) exploits properties of physical systems for computation. Evolution manipulates physical processes by stimulating materials by applying some sort of configuration signal. For materials such as liquid crystal and carbon nanotubes the properties of configuration data is rather open. In this work we investigate what kind of configuration data that most likely will be favourable for a carbon nanotube based device. An experimental approach targeting graph colouring is used to test three different types of signal representation: static voltages, square waves and a mixed signal representation. The results show that all signal representation was capable of producing a working device. In the experiments square wave representation produced the best result.

关键词： Genomics bioinformatics

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The first eNanoMapper prototype: a substance database to support safe-by-design

The first eNanoMapper prototype: a substance database to sup...

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ieee International conference on bioinformatics and Biomedicine

作者： Nina Jeliazkova Philip Doganis Bengt Fadeel Roland Grafstrom Janna Hastings Vedrin Jeliazkov Pekka Kohonen Cristian R. Munteanu Haralambos Sarimveis Bart Smeets Georgia Tsiliki David Vorgrimmler Egon Willighagen Ideaconsult Ltd. (IDEA) National Technical University of Athens (NTUA) Karolinska Institutet (KI) European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) Maastricht University (MU) in silico toxicology Gmbh (IST)

ISBN: (纸本)9781479956708

The EU-funded eNanoMapper project proposes a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs) based on open standards, ontologies and an interoperable design to enable a more effective, integrated approach to European research in nanotechnology. eNanoMapper's goal is to support the collaborative safety assessment for ENMs by creating a modular, extensible infrastructure for transparent data sharing, data analysis, and the creation of computational toxicology models for ENMs. The eNanoMapper database solution builds on previous experience of the consortium partners in supporting diverse data through flexible data storage, semantic web technologies, open source components and web services. A number of opportunities and challenges exist in nanomaterials representation and integration of ENM information, originating from diverse systems. A short summary, highlighting the pros and cons of the existing integration approaches and data models is presented. We demonstrate the approach of adopting an ontology-supported data model, describing the materials and measurements. The data sources supported include diverse formats (ISA-Tab, OECD Harmonized Templates, custom spreadsheet templates, various databases provided by consortia members). Besides retaining the data provenance, the focus on measurements provides insights into how to reuse the chemical structure database tools for nanomaterials characterization and safety.

关键词： Database Nanomaterials Safety NanoSafety Cluster

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Hadoop based enhanced cloud architecture for bioinformatic algorithms

Hadoop based enhanced cloud architecture for bioinformatic a...

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ieee Long Island systems, Applications and Technology conference (LISAT)

作者： Hamoud Alshammari Hassan Bajwa Jeongkyu Lee Department of Computer Science University of Bridgeport Bridgeport CT USA Department of Electrical Engineering University of Bridgeport Bridgeport CT USA

Explosion of biological data due to large-scale genomic research and advances in high throughput data generation tools result in massive distributed datasets. Analysis of such large non-relational, heterogeneous, and distributed datasets is emerging challenge in data driven biomedical industries. Highly complex biological data require unconventional computational approaches and knowledge-based solutions. Distributed datasets need to be reduced to smaller datasets that can be efficiently queried. Since genomic and biological data is generated in large volume and is stored in geographically diverse locations, distributed computing on multiple clusters, our objective here is to assess the feasibility of using Cloud based platform to analyze genomic big data. In this paper we present an enhanced Hadoop architecture to reduce computation by utilizing “Common Features” before performing redundant computation. The enhanced Hadoop architecture allows the jobs to share these common features among them. The common features describe the contents of data in blocks and can be used to determine DataNodes that store the required data.

关键词： bioinformatics Genomics Computer architecture Distributed databases Biological cells DNA File systems

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A Simple but Powerful Heuristic Method for Accelerating k-Means Clustering of Large-Scale Data in Life Science

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ieee-ACM TRANSACTIONS ON computational BIOLOGY AND bioinformatics 2014年第4期11卷 681-692页

作者： Ichikawa, Kazuki Morishita, Shinichi Univ Tokyo Dept Computat Biol Grad Sch Frontier Sci Kashiwa Chiba 2770882 Japan

K-means clustering has been widely used to gain insight into biological systems from large-scale life science data. To quantify the similarities among biological data sets, Pearson correlation distance and standardized Euclidean distance are used most frequently;however, optimization methods have been largely unexplored. These two distance measurements are equivalent in the sense that they yield the same k-means clustering result for identical sets of k initial centroids. Thus, an efficient algorithm used for one is applicable to the other. Several optimization methods are available for the Euclidean distance and can be used for processing the standardized Euclidean distance;however, they are not customized for this context. We instead approached the problem by studying the properties of the Pearson correlation distance, and we invented a simple but powerful heuristic method for markedly pruning unnecessary computation while retaining the final solution. Tests using real biological data sets with 50-60K vectors of dimensions 10-2001 (similar to 400 MB in size) demonstrated marked reduction in computation time for k 10-500 in comparison with other state-of-the-art pruning methods such as Elkan's and Hamerly's algorithms. The BoostKCP software is available at http://***/similar to ichikawa/boostKCP/.

关键词： bioinformatics clustering mining methods and algorithms optimization

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An efficient technique for missing value imputation in microarray gene expression data

An efficient technique for missing value imputation in micro...

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International conference on Computer and Communications Security (ICCCS)

作者： P. Valarmathie K. Dinakaran Department of Computer Science and Engineering Saveetha Engineering College Thandalam Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai

In recent years, rapid developments in genomics and proteomics have generated a large amount of biological data. Dealing with such huge data has become extremely challenging with traditional data analysis techniques. bioinformatics, aka computational biology, is the interdisciplinary science of interpreting biological data that use the tools and techniques of information technology and computer science. However, gene expressions generated by the high-throughput microarray experiments often contain missing values, which significantly affect the performance of subsequent statistical analysis and clustering algorithms. So there is a great need for estimating or imputing these missing values as accurately as possible. In general the missing values could be imputed by means of various methods namely ignoring the tuple, using the attribute mean to fill the missing value, using a global constant to fill in the missing value. In this paper a new approach called JAD (Java Application Development) imputation is proposed for missing values that can be estimated more accurately. The results show that our method JAD imputation provides a better solution to completing the microarray gene expression human serum data.

关键词： Gene expression bioinformatics Data mining conferences Computers Genomics

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Evaluating Grasp-based cloud dimensioning for comparative genomics: A practical approach

Evaluating Grasp-based cloud dimensioning for comparative ge...

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ieee International conference on Cluster Computing

作者： Rafaelli Coutinho Lúcia Drummond Yuri Frota Daniel de Oliveira Kary Ocaña IC/Fluminense Federal University Niterói Brazil COPPE/UFRJ Rio de Janeiro Brazil

Cloud computing establishes a new computing model where a wide range of computing resources are provided to several types of users. Especially for bioinformatics experiments modeled as scientific workflows, clouds provide several types of resources as virtual machines (VM), storage, databases and computing power that can be combined for empowering the scientific workflow execution. These workflows usually require high performance environments and parallelism techniques since their activities are data and computing intensive and can execute for a long time. There are then some Scientific Workflow Management systems (SWfMS) that already manage the parallel execution of scientific workflows in clouds. Most of them instantiate a virtual cluster for the execution. However, they rely on the user to estimate the amount of VMs to be instantiated to create this virtual cluster. Estimating the amount of VMs to instantiate is then a crucial task to avoid negative impacts on the workflow performance with under or over estimations. This dimensioning also is not a trivial task in clouds due to the large number of VM types to choose in a cloud provider. Previously proposed approach named GraspCC already provides a near optimal estimation of the amount of VM for general applications, not scientific workflows. In this paper, we coupled the GraspCC to SciCumulus (Cloud-based Parallel Engine for Scientific Workflows) engine to estimate the necessary amount of VMs for bioinformatics workflows. We have evaluated GraspCC by comparing the estimative with real executions of a set of large-scale comparative genomics workflows. It showed the suitability of GraspCC to estimate the amount of VMs in real bioinformatics cloud workflows.

关键词： bioinformatics Genomics Phylogeny Drugs Estimation Hidden Markov models computational modeling

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An integrated model of human Biomedical and Clinical Data structures

An integrated model of human Biomedical and Clinical Data st...

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ieee International conference on computational Advances in Bio and Medical Sciences (ICCABS)

作者： Egidijus Paliulis Hesham H. Ali Department of Information Technology Åiauliai University Åiauliai Lithuania College of Information Science and Technology University of Nebraska at Omaha Omaha NE USA

The Biomedical and Clinical (BC) research domain has evolved significantly in the last decade, quickly becoming a data-intensive field that requires sophisticated databases and data analysis tools. The constant growth of BC data has given rise to the notion of data-driven decision making. BC institutions typically use a wide range of modern diagnostic equipment that produces various types of biomedical data. Such rich data can be used to greatly improve health care. However, the development of a robust BC decision support system (BCDSS) that is driven by the available data remains a major challenge. The expanded utilization of BCDSS has been limited by the fact that current available systems are developed based on different data models and data taxonomies. In addition, the increasing availability of genetic data and the association between genotype and various diseases necessitates an integrated model that incorporates a comprehensive view of biomedical and clinical information. Such an integrated model would make it possible to develop a robust and standard BC information systems (BCIS). Standardized databases of BCIS would certainly make it much easier to take full advantage of BCDSS as well as other advances in all domains of biomedical research. This paper presents the framework for human BC data structures and an attempt to create a flexible data model that supports the notion of healthcare IT standards and accommodates the design/development of domain-specific BC databases.

关键词： Data models Diseases Standards Databases Medical diagnostic imaging bioinformatics

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