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Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in bioinformatics applications

IEEE TRANSACTIONS ON CYBERNETICS 2014年第3期44卷 445-455页

作者： Yang, Pengyi Yoo, Paul D. Fernando, Juanita Zhou, Bing B. Zhang, Zili Zomaya, Albert Y. Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Khalifa Univ Sci Dept Elect & Comp Engn Res & Technol Abu Dhabi 127788 U Arab Emirates Monash Univ Fac Med Nursing & Hlth Sci Clayton Vic 3800 Australia Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Sydney Fac Comp & Informat Sci Chongqing Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3217 Australia

Data sampling is a widely used technique in a broad range of machine learning problems. Traditional sampling approaches generally rely on random resampling from a given dataset. However, these approaches do not take into consideration additional information, such as sample quality and usefulness. We recently proposed a data sampling technique, called sample subset optimization (SSO). The SSO technique relies on a cross-validation procedure for identifying and selecting the most useful samples as subsets. In this paper, we describe the application of SSO techniques to imbalanced and ensemble learning problems, respectively. For imbalanced learning, the SSO technique is employed as an under-sampling technique for identifying a subset of highly discriminative samples in the majority class. In ensemble learning, the SSO technique is utilized as a generic ensemble technique where multiple optimized subsets of samples from each class are selected for building an ensemble classifier. We demonstrate the utilities and advantages of the proposed techniques on a variety of bioinformatics applications where class imbalance, small sample size, and noisy data are prevalent.

关键词： bioinformatics applications ensemble learning imbalanced learning sample subset optimization (SSO) undersampling

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BioNimbuZ: A Federated Cloud Platform for bioinformatics applications

BioNimbuZ: A Federated Cloud Platform for Bioinformatics App...

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IEEE International Conference on bioinformatics and Biomedicine (IEEE BIBM)

作者： Rosa, Michel Moura, Breno Vergara, Guilherme Santos, Lucas Ribeiro, Edward Holanda, Maristela Walter, Maria Emilia Araujo, Aleteia Univ Brasilia UnB Dept Comp Sci Brasilia DF Brazil

ISBN: (纸本)9781509016105

Challenges in bioinformatics include tools to treat large-scale processing, mainly due to the large volumes of data generated by high-throughput sequencing machines. Besides, many of these tools are not user friendly, and do not distribute their workloads properly. In federated cloud environments, even though services and resources are shared and available online, the processes of a workflow execution are almost entirely not automated, and the majority of these processes do not efficiently balance their workloads. This paper presents the federated cloud platform, called BioNimbuZ, a hybrid platform designed to execute bioinformatics applications easily and efficiently, with good workload balance. Our tests were performed using a real bioinformatics workflow, with fragments generated by the Illumina sequencer, having achieved good performance in practice.

关键词： BioNimbuZ cloud computing federated cloud computing bioinformatics applications

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Exploring the Use of Remote GPU Virtualization in Low-Power Systems for bioinformatics applications 18

Exploring the Use of Remote GPU Virtualization in Low-Power ...

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47th International Conference on Parallel Processing (ICPP) / International Workshop on Embedded Multicore Systems (EMS)

作者： Reano, Carlos Prades, Javier Silla, Federico Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9781450365239

The large power requirements of current High-Performance Computing (HPC) deployments has motivated that several supercomputers and research proposals consider the use of low-power processor architectures instead of the traditional mainstream, but power hungry, x86 designs. However, many of these proposals do not consider the use of GPUs (Graphics Processing Units). GPUs are power efficient devices while in use and, therefore, considering them is aligned with the power-efficiency efforts that motivated the use of low-power processors. In this paper we explore the use of GPUs in several low-power processor architectures in the context of the bioinformatics domain. To that end, we make use of the rCUDA remote GPU virtualization middleware to provide these low-power systems with access to GPU accelerators. In this study we leverage three different low-power processor architectures to execute three bioinformatics applications. The result of this exploration is that the Xeon D processor architecture is well suited for this purpose whereas other low-power architectures such as the Atom or ARM ones still need to be improved.

关键词： CUDA rCUDA low-power processors bioinformatics applications GPU virtualization

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A Novel Implementation of Double Precision and Real Valued ICA Algorithm for bioinformatics applications on GPUs

A Novel Implementation of Double Precision and Real Valued I...

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18th International Conference on Euro-Par Parallel Processing

作者： Foshati, Amin Khunjush, Farshad Shiraz Univ Dept Comp Sci & Engn Int Div Shiraz Iran Hormozgan Univ Dept Elect Engn Bandar Abbas Iran Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz Iran IPM Inst Res Fundamental Sci Sch Comp Sci Tehran Iran

ISBN: (纸本)9783642369490

Several applications in the field of bioinformatics require extracting individual source signals from a large amount of observed data (signal mixtures). Among the available solutions, a possible approach is the independent component analysis (ICA). However, this computationally intensive algorithm does not fit for many real-time or large size data applications. As a result, this shortcoming calls for speeding up the execution of this algorithm. Recently, graphics processing units (GPUs) have emerged as general-purpose parallel processing accelerators. This platform has the potentials to be leveraged in processing a large amount of signals received from medical devices such as EEG and ECG tools. This work provides the implementation of an ICA algorithm, Joint Approximate Diagonalization of Eigen-matrices (JADE), on a low cost programmable graphics cards using CUDA programming toolkits. For this implementation, we achieved an overall speedup of over 7.9x for estimating 64 components, each with 9760 samples.

关键词： Independent Component Analysis (ICA) JADE Algorithm Blind Source Separation (BSS) Graphics Processing Units (GPUs) CUDA bioinformatics applications

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Distributed, on-demand, data-intensive and collaborative simulation analysis

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2003年第6期19卷 849-859页

作者： Breckenridge, A Pierson, L Sanielevici, S Welling, J Keller, R Woessner, U Schulze, J Sandia Natl Labs Albuquerque NM 87185 USA Pittsburgh Supercomp Ctr Pittsburgh PA USA Ctr High Performance Comp Stuttgart Germany

Distributed, on-demand, data-intensive, and collaborative simulation analysis tools are being developed by an international team to solve real problems such as bioinformatics applications. The project consists of three distinct focuses: compute, visualize, and collaborate. Each component utilizes software and hardware that performs across the International Grid. Computers in North America, Asia, and Europe are working on a common simulation programs. The results are visualized in a multi-way 3D visualization collaboration session where additional compute requests can be submitted in real-time. Navigation controls and data replication issues are addressed and solved with a scalable solution. Published by Elsevier B.V.

关键词： compute visualize collaborate data-intensive simulation analysis bioinformatics applications

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Simultaneous scheduling of replication and computation for data-intensive applications on the grid

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Journal of Grid Computing 2006年第1期4卷 19-31页

作者： Desprez, Frédéric Vernois, Antoine LIP Laboratory/GRAAL Project UMR CNRS Univ. Claude Bernard Lyon 1 F-69364 Lyon Cedex 07 46 Allée d'Italie France

Managing large datasets has become one major application of Grids. Life science applications usually manage large databases that should be replicated to scale applications. The growing number of users and the simple access to Internet-based application has stressed Grid middleware. Such environment are thus asked to manage data and schedule computation tasks at the same time. These two important operations have to be tightly coupled. This paper presents an algorithm (Scheduling and Replication Algorithm, SRA) that combines data management and scheduling using a steady-state approach. Using a model of the platform, the number of requests as well as their distribution, the number and size of databases, we define a linear program to satisfy all the constraints at every level of the platform in steady-state. The solution of this linear program will give us a placement for the databases on the servers as well as providing, for each kind of job, the server on which they should be executed. Our theoretical results are validated using simulation and logs from a large life science application. © Springer Science+Business Media, Inc. 2006.

关键词： bioinformatics applications Data management Grid computing Scheduling

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Distributed, on-demand, data-intensive and collaborative simulation analysis

Distributed, on-demand, data-intensive and collaborative sim...

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3rd Biennial International Grid Application-Driven Testbed Conference (iGrid 2002)

关键词： compute visualize collaborate data-intensive simulation analysis bioinformatics applications

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Quantitative Analysis of Sequence Alignment applications on Multiprocessor Architectures 09

Quantitative Analysis of Sequence Alignment Applications on ...

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6th ACM International Conference on Computing Frontiers and Workshops

作者： Sanchez Castano, Friman Ramirez, Alex Valero Cortes, Mateo Tech Univ Catalonia Comp Architecture Dept Barcelona Spain

ISBN: (纸本)9781605584133

The exponential growth of databases that contains biological information (such as protein and DNA data) demands great efforts to improve the performance of computational platforms. In this work we investigate how bioinformatics applications benefit from parallel architectures that combine different alternatives to exploit coarse-and fine-grain parallelism. As a case of analysis we study the performance behavior of the Ssearch application that implements the Smith-Waterman algorithm, which is a dynamic programing approach that explores the similarity between a pair of sequences. The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). We study how this algorithm can take advantage of different parallel machines like the SGI Altix, IBM Power6, Cell BE machines and MareNostrum. Our results show that a share memory architecture like the PowerPC 970MP of Marenostrum can surpass a heterogeneous, machine like the current Cell BE. Our quantitative analysis includes not only a Study of scalability of the performance in terms of speedup, but also includes the analysis of bottlenecks in the execution of the application. This analysis is carried out through the study of the execution phases that the application presents.

关键词： multiprocessor architectures parallel architectures bioinformatics applications sequence comparison

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An indexation and discovery architecture for Semantic Web Services and its application in bioinformatics

An indexation and discovery architecture for Semantic Web Se...

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IEEE International Conference on Granular Computing

作者： Yu, Liyang Sunderraman, Rajshekhar Wang, Haibin Georgia State Univ Dept Comp Sci 34 Peachtree StSuite 1450 Atlanta GA 30303 USA Dept Comp Sci Atlanta GA 30303 USA Emory Univ Winship Canc Ctr Biostat Res & Informat Atlanta GA 30322 USA

ISBN: (纸本)142440133X

Recently much research effort has been devoted to the discovery of relevant Web services. It is also widely recognized that adding semantics to Web service description is the solution to this challenge. Web services with explicit semantic annotation are called Semantic Web Services (SWS). In this paper, we propose an indexation and discovery architecture for semantic Web services, together with an application example in the area of bioinformatics. In our approach, a SWS repository is created and maintained by crawling both the ontology-oriented UDDI registries and the Web sites that hosting SWS. When presented with a service request, the proposed system will invoke the matching algorithm and a candidate set will be returned with different degree of matching being considered. We believe this approach can add more flexibility to the current industry standards by offering more choices to both the service requesters and publishers. Also, the framework in this approach is extensible: different matching algorithms can be easily added, and necessary QoS models can be built on top of the matching algorithms.

关键词： Web services standards semantic Web services OWL-S ontology indexation service discovery Web crawler bioinformatics applications

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An Indexation and Discovery Architecture for Semantic Web Services and its Application in bioinformatics

An Indexation and Discovery Architecture for Semantic Web Se...

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作者： Liyang Yu Georgia State University

学位级别：硕士

Recently much research effort has been devoted to the discovery of relevant Web services. It is widely recognized that adding semantics to service description is the solution to this challenge. Web services with explicit semantic annotation are called Semantic Web Services (SWS). This research proposes an indexation and discovery architecture for SWS, together with a prototype application in the area of bioinformatics. In this approach, a SWS repository is created and maintained by crawling both ontology-oriented UDDI registries and Web sites that hosting SWS. For a given service request, the proposed system invokes the matching algorithm and a candidate set is returned with different degree of matching considered. This approach can add more flexibility to the current industry standards by offering more choices to both the service requesters and publishers. Also, the prototype developed in this research shows the value can be added by using SWS in application areas such as bioinformatics.

关键词： Web service standards Semantic Web Semantic Web services OWL-S Ontology ation Service discovery Search engine Web crawler bioinformatics applications

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