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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 879 条记录，以下是221-230 订阅

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4th International conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2015

4th International Conference on Frontiers in Intelligent Com...

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4th International conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2015

ISBN: (纸本)9788132226932

The proceedings contain 61 papers. The special focus in this conference is on Advances in Nature Inspired Algorithms for Engineering Optimization Problems, bioinformatics and computational Biology. The topics include: An efficient dynamic scheduling algorithm for soft real-time tasks in multiprocessor system using hybrid quantum-inspired genetic algorithm;equitable machine learning algorithms to probe over P2P botnets;directed search-based PSO algorithm and its application to scheduling independent task in multiprocessor environment;prediction of phosphorylation sites using a consensus of MLP classifiers;SVM-based pre-microrna classifier using sequence, structural, and thermodynamic parameters;a novel differentially coexpressed gene module detection algorithm using spectral clustering;intelligent topological differential gene networks;impact of threshold to identify vocal tract;application of compressed sensing in cognitive radio;compressed sensing-based NBI mitigation in ultra-wideband energy detector;a novel data mining scheme for smartphone activity recognition by accelerometer sensor;mining and ranking association rules in support, confidence, correlation, and dissociation framework;mining closed interesting subspaces to discover conducive living environment of migratory animals;frequent patterns mining from data cube using aggregation and directed graph;a modified parallel thinning method for handwritten oriya character images;offline signature verification using artificial neural network;a simple and effective technique for online handwritten bangla character recognition;offline writer identification from isolated characters using textural features and application of krill herd algorithm for optimum design of load frequency controller for multi-area power system network with generation rate constraint.

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13th International Work-conference on Artificial Neural Networks, IWANN 2015 13

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13th International Work-conference on Artificial Neural Networks, IWANN 2015

ISBN: (纸本)9783319192574

The proceedings contain 57 papers. The special focus in this conference is on Computing Languages with Bio-Inspired Devices, Brain-Computer Interfaces, Multi-Robot systems, Video and Image Processing. The topics include: Grammatical inference model for measuring language complexity;a proposal for contextual grammatical inference;training in realistic virtual environments;real-time monitoring of biomedical signals to improve road safety;authentication of brain-computer interface users in network applications;a first step toward a possibilistic swarm multi-robot task allocation;a bottom-up robot architecture based on learnt behaviors driven design;from human eye fixation to human-like autonomous artificial vision;a mini robot for scientific applications;visualization of complex datasets with the self-organizing spanning tree;a detection system for vertical slot fishways using laser technology and computer vision techniques;interactive relevance visual learning for image retrieval;scene classification based on local binary pattern and improved bag of visual words;a novel framework for hyperemia grading based on artificial neural networks;finding the texture features characterizing the most homogeneous texture segment in the image;bio-inspired motion estimation with event-driven sensors;domain generalization based on transfer component analysis;deep transfer learning ensemble for classification;transfer learning for the recognition of immunogold particles in TEM imaging;improved retrieval for challenging scenarios in clique-based neural networks;deep neural networks for wind energy prediction and convolutional neural networks for detecting and mapping crowds in first person vision applications.

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Biomarker Discovery, High Performance and Cloud Computing: A Comprehensive Review

Biomarker Discovery, High Performance and Cloud Computing: A...

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ieee International conference on bioinformatics and Biomedicine

作者： Jaine Blayney Valeriia Haberland Gaye Lightbody Fiona Browne School of Medicine Dentistry and Biomedical Sciences Queen's University Belfast Tungsten Centre for Intelligent Data Analytics Goldsmiths University of London School of Computing and Mathematics Ulster University

ISBN: (纸本)9781467368001

The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

关键词： Genomics bioinformatics Sequential analysis Robustness Decision support systems

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Predicting microRNA-disease associations by integrating multiple biological information

Predicting microRNA-disease associations by integrating mult...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jianxin Wang Min Li Jin Liu Yi Pan School of Information Science and Engineering Central South University Changsha Human P.R. China Department of Computer Science Georgia State University Atlanta GA USA

MicroRNAs (miRNAs) are a set of small non-coding RNAs that play critical roles in many human diseases. Identifying potential miRNA-disease association is helpful to explore the underlying molecular mechanisms of disease. Currently, it is expensive and time-consuming to detect miRNA-disease associations with experimental methods. On the other hand, many known associations between miRNAs and diseases provide useful information for new miRNA-disease interaction discovery. In this study, we propose a computational framework to infer the relationship between miRNA and disease by integrating multiple data resources. We use sequence and function information of miRNA and semantic and function information of disease to measure similarity of miRNA and disease, respectively. In addition, kernelized Bayesian matrix factorization method is employed to infer potential miRNA-disease association by integrating these data resources. The experimental results demonstrate that our method can effectively predict unknown miRNA-disease association.

关键词： Diseases Genomics bioinformatics Polymers Decision support systems Breast

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Semantic mapping to align PPI networks and predict conserved protein complexes

Semantic mapping to align PPI networks and predict conserved...

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ieee International conference on bioinformatics and Biomedicine

作者： Lizhu Ma Young-Rae Cho Department of Computer Science Baylor University Waco TX USA

ISBN: (纸本)9781467368001

PPI networks are significant resources to determine molecular organizations in a cell. The availability of genome-wide PPI networks on diverse model species have provided a new paradigm to identify evolutionarily conserved substructures. computational methods for cross-species comparison of PPI networks have recently been applied to prediction of conserved protein complexes. These methods use network alignment techniques by mapping homologous proteins. We propose a novel network alignment approach by semantic mapping between proteins from different species. We apply this approach to predict conserved human protein complexes by aligning yeast PPI networks representing well-studied protein complexes with a human PPI network in a genomic scale. In our experiments, we used a recently proposed integrative semantic similarity measure, simICND, for semantic mapping. The experimental results show that the proposed network alignment approach has higher accuracy on predicting human protein complexes than other clustering-based methods. The experimental results also show that the proposed approach has higher efficiency than previous network alignment algorithms. This study provides a valuable framework to discover conserved systems from a functional standpoint.

关键词： bioinformatics Genomics Pipelines Proteins PPI networks network alignment protein-protein interactions semantic similarity

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Protein-protein interaction network inference from multiple kernels with optimization based on random walk by linear programming

Protein-protein interaction network inference from multiple ...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Lei Huang Li Liao Cathy H. Wu Department of Computer and Information Sciences University of Delaware Newark Delaware

Reconstruction of PPI networks is a central task in systems biology, and inference from multiple heterogeneous data sources offers a promising computational approach to making de novo PPI prediction by leveraging complementary information and the partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial network. Our proposed method utilizes Baker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network, including gene expression and Pfam domain profiles, are then combined into the form of a weighted kernel, which provides a new “adjacency matrix” for the whole network but is required to comply with the transition matrix on the part of the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernel. The minimization problem is solved by linear programming. The weighted kernel is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network. The results on synthetic data and real data from Yeast show that the accuracy of PPI prediction measured by AUC is increased by up to 19% as compared to a control method without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations between PPIs and various feature kernels, thereby improving PPI prediction.

关键词： bioinformatics Proteins Genomics Weight measurement Electronics packaging Kernel

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A computational approach to prioritize functionally significant variations in whole exome sequencing

A computational approach to prioritize functionally signific...

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International conference on Industrial and Information systems, ICIIS

作者： Ishani Liyanage Rupika Wijesinghe Ruvan Weerasinghe Nilakshi Samaranayake University of Colombo School of Computing (UCSC) Colombo 7 Sri Lanka Department of Parasitology Faculty of Medicine University of Colombo Colombo Sri Lanka

Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variants which are broadly used for studying common and complex diseases. However, the tremendous number of SNPs in the human genome poses challenges to perform extensive analysis on all SNPs. Exome sequencing strategies are capable of identifying unknown SNPs which have an impact on the protein function and cause various diseases conditions. However, identifying genuine disease mutations or variants is still laborious and challenging. Here, we propose a prioritization model in order to predict functionally significant SNPs in whole exome sequencing. Our experimental results show that the proposed SNP prioritization model is effective in reliable identification of functionally significant SNPs which are more likely to be associated with disease conditions or functional impairments in massive amount of exome sequencing data. The proposed model will enable researchers and geneticists to conduct their follow up studies easily by reducing their experimental and analysis overhead.

关键词： Genomics bioinformatics Diseases Sequential analysis Support vector machines

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ATria: A novel centrality algorithm applied to biological networks

ATria: A novel centrality algorithm applied to biological ne...

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

作者： Trevor Cickovski Eli Peake Vanessa Aguiar-Pulido Giri Narasimhan Department of Computer Science Eckerd College St. Petersburg FL USA Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute Florida International University Miami FL USA

Large-scale biological networks such as gene regulatory and PPI have become commonplace through systems biology. Figure 1 shows a microbial social network, which attempts to infer interactions between microbes within a community from metagenomics studies. In particular this is a co-occurence network, where green/red edges respectively represent positive/negative correlations, or how strongly two bacterial taxa tend to co-infect samples. We may be interested in bacterial taxa that drive community behavior, initial infectors of a community, or the effects of structural changes. Answers to these questions can come from discovering ”important” nodes in these biological networks, which is the goal of social network centrality. Depending on the definition of importance used, many different centrality notions currently exist. We suggest three notions that are potentially important to biological networks, and especially to microbial social networks: 1) For each club or high density subgraph, a dominant or leader node that is responsible for connecting many individuals and driving club behavior. 2) A villain node or common enemy, defined as having many strong negative edges to a club. 3) A bridge node that connects two or more clubs, thus having the ability to link different social circles.

关键词： Social network services Bridges Electronic mail Microorganisms Clustering algorithms Computer science

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Big Data: Cloud computing in genomics applications

Big Data: Cloud computing in genomics applications

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ieee International conference on Big Data

作者： Hangu Yeo Catherine H. Crawford Department of Next Generation Systems IBM T. J. Watson Research Center Yorktown Heights NY U.S.A

Healthcare applications typically require big data management as well as intensive computation. This is especially true with recently developed next generation sequencing technology which increases interests in processing the huge amount of information in a timely fashion. In this paper, we focus on testing whether the healthcare applications can scale well on commercial big data platforms that implement MapReduce framework. We selected short read sequence alignment and assembly workloads in genome analysis workloads, and chose Bowtie, Blast and Contrail-bio which are publically available applications designed to run on the Hadoop MapReduce framework. To speed-up the processes we compressed the intermediate data using various compression schemes the compression schemes are compared. The test results are very promising and indicate that the wide range of genomic analysis workflows can be optimized on MapReduce frameworks with great computational efficiency and scalability.

关键词： bioinformatics Genomics Assembly Sequential analysis Next generation networking Big data Cloud computing

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Science gateways - Leveraging modeling and simulations in HPC infrastructures via increased usability 13

Science gateways - Leveraging modeling and simulations in HP...

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13th International conference on High Performance Computing and Simulation, HPCS 2015

作者： Gesing, Sandra Dooley, Rion Pierce, Marlon Krüger, Jens Grunzke, Richard Herres-Pawlis, Sonja Hoffmann, Alexander University of Notre Dame 123 Information Technology Center Notre DameIN46556 United States AustinTX78758 United States Indiana University Cyberinfrastructure Building 2719 East 10th Street BloomingtonIN47408 United States Applied Bioinformatics Tübingen University of Tübingen Sand 14 Tübingen72076 Germany Center for Information Services and High Performance Computing Technische Universität Dresden Zellescher Weg 12-14 Dresden01062 Germany Institut für Anorganische Chemie Rheinisch-Westfälische Technische Hochschule Aachen Landoltweg 1 Aachen52074 Germany

ISBN: (纸本)9781467378123

Modeling and simulations, which necessitate HPC infrastructures, are often based on complex scientific theories and involve interdisciplinary research teams. IT specialists support with the efficient access to HPC infrastructures. They design, implement and configure the simulations and models reflecting the sophisticated theoretical models and approaches developed and applied by domain researchers. Roles in such interdisciplinary teams may overlap dependent on the knowledge and experience with computational resources and/or the research domain. Bioinformaticians, for example, are in general trained to act as IT specialists, while having also a good knowledge about biology and chemistry to support the user community competently. Domain researchers are mainly not IT specialists and the requirement to employ HPC infrastructures via command line often forms a huge hurdle for them. Thus, there is the need to increase the usability of simulations and models on HPC infrastructures for the uptake by the user community. Science gateways form a solution, which offer a graphical user interface tailored to a specific research domain with a single point of entry for job and data management hiding the underlying infrastructure. In the last 10 years quite a few web development frameworks, science gateway frameworks and APIs with different foci and strengths have evolved to support the developers of science gateways in implementing an intuitive solution for a target research domain. The selection of a suitable technology for a specific use case is essential and helps reducing the effort in implementing the science gateway by re-using existing software or frameworks. Thus, a solution for a user community can be provided more efficiently. This paper goes into detail for science gateway concepts as well as information resources, gives examples for successful technologies and proposes criteria for choosing a technology for a use case. © 2015 ieee.

关键词： Large scale systems

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