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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 - Medical Informatics and Decision Making

作者： Blayney, Jaine Haberland, Valeriia Lightbody, Gaye Browne, Fiona Queens Univ Belfast Sch Med Dent & Biomed Sci Belfast BT7 1NN Antrim North Ireland Univ London Tungsten Ctr Intelligent Data Analyt London WC1E 7HU England Univ Ulster Sch Comp & Math Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9781467367981

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： bioinformatics Decision support systems Genomics Robustness Sequential analysis

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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 - Medical Informatics and Decision Making

作者： Huang, Lei Liao, Li Wu, Cathy H. Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Delaware Ctr Bioinformat & Computat Biol Newark DE 19711 USA

ISBN: (纸本)9781467367981

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 Electronics packaging Genomics Kernel Proteins Weight measurement Data sources systems biology Proteins Random walk linear programming Genomics Seeds pulse position indicator Kernel S matrix Electronics packaging Weighing bioinformatics

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Next Generation Biobanks 3

Next Generation Biobanks

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ieee International conference on Big Data

作者： Seebode, Christian Ort, Matthias Hufnagl, Peter Regenbrecht, Christian R. A. ORTEC Med Berlin Germany Charite Inst Pathol Berlin Germany

ISBN: (纸本)9781479999255

Biobanks have a long tradition of being mere repositories of cells and tissues. Their best use is in retrospective studies, where stored tissues and cells can be used to address scientific questions, but this paradigm has been changing lately. Biobanks need to inhabit a different role in the scientific process. In fact the information contained in biobanks can fuel the scientific reasoning itself more than just accompanying with management of specimens and data. We present a biobank architecture based on our product Health Intelligence Platform (HIP) that supports these requirements. We describe a way to support scientific reasoning directly with HIP and integrate sample management in a way that it contributes to scientific reasoning and outcome.

关键词： Health information management bioinformatics computational systems biology

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2014 ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2014

2014 IEEE Conference on Computational Intelligence in Bioinf...

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9th ieee International conference on Distributed Computing in Sensor systems, DCOSS 2014

ISBN: (纸本)9781479946198

The proceedings contain 55 papers. The topics discussed include: low-power listening goes multi-channel;PhyTraces: simulating new RF environments with physical layer traces;a novel wake-up receiver with addressing capability for wireless sensor nodes;symmetric coherent link degree, adaptive throughput-transmission power for wireless sensor networks;indoor occupant positioning system using active RFID deployment and particle filters;harnessing non-uniform transmit power levels for improved sequence based localization;FlashTrack: a fast, in-network tracking system for sensor networks;crowd-sensing with polarized sources;a dual-sensor enabled indoor localization system with Crowdsensing spot survey;on decentralized in-network aggregation in real-world scenarios with crowd mobility;and the information funnel: exploiting named data for information-maximizing data collection.

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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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SuperPhy A Pilot Resource for Integrated Phylogenetic and Epidemiological Analysis of Pathogens

SuperPhy <i>A Pilot Resource for Integrated Phylogenetic and...

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5th International conference on bioinformatics Models, Methods and Algorithms (bioinformatics) is part of 7th International Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC)

作者： Whiteside, Matthew D. Laing, Chad R. Manji, Akiff Gannon, Victor P. J. Publ Hlth Agcy Canada Lab Foodborne Zoonoses Lethbridge AB Canada

ISBN: (纸本)9789897580123

Advances in DNA sequencing technology have created new opportunities in fields such as clinical medicine and epidemiology, where performing real-time, genome-based surveillance and identification of phenotypic characteristics of bacterial pathogens is now possible. New analytical tools and infrastructure are needed to analyze these genomic datasets, store the data, and provide the essential biological information to end-users. We have implemented an online whole-genome analyses platform called SuperPhy that uses Panseq as an engine to compare bacterial genomes, the Fisher's exact test to identify sub-group specific loci, and FastTree to create maximum-likelihood trees. SuperPhy facilitates the upload of genomes for both private and public use. Analyses include: 1) genomic comparisons of clinical isolates, and identification of virulence and antimicrobial resistance genes in silico, 2) associations between specific genotypes and phenotypic meta-data (e. g., geospatial distribution, host, source);3) identification of group-specific genome markers (presence/absence of specific genomic regions, and single-nucleotide polymorphisms) in bacterial populations;4) the ability to manipulate the display of phylogenetic trees;5) identify statistically significant clade-specific markers. The SuperPhy pilot database currently contains genome sequences for 1063 Escherichia coli strains. Future work will extend SuperPhy to include multiple pathogens.

关键词： bioinformatics computational Biology Population Genomics Epidemiology Phylogeny Bacterial Pathogenesis

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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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HPC+Azure environment for bioinformatics applications

HPC+Azure environment for bioinformatics applications

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2013 ieee International conference on bioinformatics and Biomedicine, ieee BIBM 2013

作者： Sidhu, Amandeep S. Balakrishnan, Suresh Reuben Dhillon, Sarinder K. School of Engineering and Science Curtin University Sarawak Malaysia Faculty of Health Sciences Curtin University Perth WA Australia Faculty of Science University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781479913091

In the past 20 years, huge flow of data, produced by the nonstop rise of computational power has led to a paradigm shift in large scale data processing mechanisms and computing architecture. As a result, human and computational resources are needed to aid data-intensive operations which will cause the high degree of storage and management expenses. An organized and standard approach is important to manage these issues with an architecture that able to scale into the predictable future. Instead of the fastest and largest single computer solution, economical clusters of computers can better manage and process all data. Most of the high-performance computing (HPC) systems need a huge amount of processing power and Windows Azure is capable of providing a huge quantity of processing power on demand. As the Windows HPC server and Windows Azure combine, the cloud and on-premises world are now able to function together. In this paper we explore a HPC+Azure implementation model and demonstrate by running a genome sequence assembly application. © 2013 ieee.

关键词： Digital storage

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13th ieee International conference on bioinformatics and BioEngineering, ieee BIBE 2013

13th IEEE International Conference on BioInformatics and Bio...

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13th ieee International conference on bioinformatics and BioEngineering, ieee BIBE 2013

ISBN: (纸本)9781479931637

The proceedings contain 184 papers. The topics discussed include: systems genetics: experimental and computational challenges;emerging trends for decentralized e-health services in smart cities;a profile-based trust management scheme for ubiquitous healthcare environment;CloudStudy: a cloud-based system for supporting multi-centre studies;an architecture for designing future Internet (FI) applications in sensitive domains: expressing the software to data paradigm by utilizing hybrid cloud technology;quering functional brain connectomics to discover consistent subgraph patterns;a system for optically controlling neural circuits with very high spatial and temporal resolution;towards an overall 3-D vector field reconstruction via discretization and a linear equations system;a low-cost embedded real-time 3D stereo matching system for surveillance applications;and a wearable ultrasound multi-transducer array system for abdominal organs monitoring.

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An energy-aware bioinformatics application for assembling short reads in high performance computing systems

An energy-aware bioinformatics application for assembling sh...

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2012 10th Annual International conference on High Performance Computing and Simulation, HPCS 2012

作者： Warnke, Julia Pawaskar, Sachin Ali, Hesham College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 United States

ISBN: (纸本)9781467323598

Current biomedical technologies are producing massive amounts of data on an unprecedented scale. The increasing complexity and growth rate of biological data has made bioinformatics data processing and analysis a key and computationally intensive task. High performance computing (HPC) has been successfully applied to major bioinformatics applications to reduce computational burden. However, a naïve approach for developing parallel bioinformatics applications may achieve a high degree of parallelism while unnecessarily expending computational resources and consuming high levels of energy. As the wealth of biological data and associated computational burden continues to increase, there has become a need for the development of energy efficient computational approaches in the bioinformatics domain. To address this issue, we have developed an energy-aware scheduling (EAS) model to run computationally intensive applications that takes both deadline requirements and energy factors into consideration. An example of a computationally demanding process that would benefit from our scheduling model is the assembly of short sequencing reads produced by next generation sequencing technologies. Next generation sequencing produces a very large number of short DNA reads from a biological sample. Multiple overlapping fragments must be aligned and merged into long stretches of contiguous sequence before any useful information can be gathered. The assembly problem is extremely difficult due to the complex nature of underlying genome structure and inherent biological error present in current sequencing technologies. We apply our EAS model to a newly proposed assembly algorithm called Merge and Traverse, giving us the ability to generate speedup profiles. Our EAS model was also able to dynamically adjust the number of nodes needed to meet given deadlines for different sets of reads. © 2012 ieee.

关键词： bioinformatics

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