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Agent and Spatial Based Parallelization of Biological Network Motif Search

Matthew Kipps, Wooyoung Kim, Munehiro Fukuda
2015 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems  
To demonstrate the MASS library's fitness to graph parallelization, we have focused on biological network motif search.  ...  To bridge the semantic gap between the original sequential algorithms and their corresponding parallelized programs, we have been developing MASS: a parallel library for multi-agent spatial simulation.  ...  This paper intends to demonstrate the fitness of agent-based approach to graph parallelization, focusing on biological network motif search in particular finding all subgraphs in a target network.  ... 
doi:10.1109/hpcc-css-icess.2015.222 dblp:conf/hpcc/KippsKF15 fatcat:mzmikzjllnhnvovorq3jlgcrsu

Editorial

Ning Xiong, Defu Zhang, Libo Wang
2016 Neural Processing Letters  
It is a multidisciplinary research area, attracting researchers from both biological learning systems and artificial learning systems.  ...  On the other hand, phenomena and principles found from nature offer a valuable insight and perspective of how effective learning can be conducted in both biological and artificial systems.  ...  The paper "A resource aware MapReduce based parallel SVM for large scale image classifications" by Guo et al. presents a resource aware MapReduce based parallel support vector machine algorithm (RASMO)  ... 
doi:10.1007/s11063-016-9533-y fatcat:63jnzu5ssrd2ditv2t3h2m35r4

Scalable Graph Algorithms [article]

Christian Schulz
2019 arXiv   pre-print
As huge networks become abundant, there is a need for scalable algorithms to perform analysis.  ...  Complex graphs are useful in a wide range of applications from technological networks to biological systems like the human brain.  ...  In [1, 10] we present a new multilevel algorithm designed for partitioning complex networks. Its rationale is to use aggressive cluster-based coarsening and simple, yet effective local search.  ... 
arXiv:1912.00245v1 fatcat:mop2e6pqtnbr7irkrakraael6a

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

Ariful Azad, Georgios A Pavlopoulos, Christos A Ouzounis, Nikos C Kyrpides, Aydin Buluç
2018 Nucleic Acids Research  
Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers.  ...  By exploiting distributedmemory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks.  ...  SPICi (11) for example, is a fast, local network clustering algorithm that detects densely connected communities within a network.  ... 
doi:10.1093/nar/gkx1313 pmid:29315405 pmcid:PMC5888241 fatcat:fwlt3x7tfjes3ikfbtvp6gzv3q

Efficiency of Parallel Direct Optimization

Daniel A. Janies, Ward C. Wheeler
2001 Cladistics  
Graphs of parallel efficiency of parallel building and multibuilding on the large cluster for three data sets: 130 hexapods, 264 mammals, and 500 angiosperms.  ...  Graphs of parallel efficiency of ratcheting and multiratcheting on the large cluster for three data sets: 130 hexapods, 264 mammals, and 500 angiosperms.  ...  Branch swapping provement in tree search algorithms in the past 2 years. in parallel shows excellent speed-up for 16 slave proces-These improvements include the use of genetic algosors on the large cluster  ... 
doi:10.1111/j.1096-0031.2001.tb00106.x fatcat:qnmoiwubfrbspkgkg3shpt7toq

Efficiency of Parallel Direct Optimization

D Janies
2001 Cladistics  
Graphs of parallel efficiency of parallel building and multibuilding on the large cluster for three data sets: 130 hexapods, 264 mammals, and 500 angiosperms.  ...  Graphs of parallel efficiency of ratcheting and multiratcheting on the large cluster for three data sets: 130 hexapods, 264 mammals, and 500 angiosperms.  ...  Branch swapping provement in tree search algorithms in the past 2 years. in parallel shows excellent speed-up for 16 slave proces-These improvements include the use of genetic algosors on the large cluster  ... 
doi:10.1006/clad.2000.0160 pmid:12240679 fatcat:lcoohimb7zdqjmlxncnou3fuza

Evaluating balancing on social networks through the efficient solution of correlation clustering problems

Mario Levorato, Rosa Figueiredo, Yuri Frota, Lúcia Drummond
2017 EURO Journal on Computational Optimization  
One challenge for social network researchers is to evaluate balance in a social network.  ...  Then, by using our algorithms, we solve the problem of measuring the structural balance on large real-world social networks.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  ... 
doi:10.1007/s13675-017-0082-6 fatcat:ujmbnkx7rvcp7egw3z5lqfivqm

SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

David A. Bader, Kamesh Madduri
2008 Proceedings, International Parallel and Distributed Processing Symposium (IPDPS)  
To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering  ...  For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph  ...  We thank Nicolas Bitouze (ENS Cachan) for implementing the parallel greedy agglomeration algorithm, and Jon Berry and Bruce Hendrickson (Sandia National Laboratories) for discussions on large-scale graph  ... 
doi:10.1109/ipdps.2008.4536261 dblp:conf/ipps/BaderM08 fatcat:gceim62ogjdybe4nfyx224t4wa

Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA

Aimin Yang, Wei Zhang, Jiahao Wang, Ke Yang, Yang Han, Limin Zhang
2020 Frontiers in Bioengineering and Biotechnology  
Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage.  ...  Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge.  ...  Smith and Waterman (1981) improved the dynamic programming algorithm to make it into a local optimal algorithm, which can search for sequence fragments with the high local similarity between two sequences  ... 
doi:10.3389/fbioe.2020.01032 pmid:33015010 pmcid:PMC7498545 fatcat:74n23fw6ibeeznotkcmwsyrqpm

Page 156 of American Society of Civil Engineers. Collected Journals Vol. 8, Issue 3 [page]

1995 American Society of Civil Engineers. Collected Journals  
This paper presents a distributed genetic algorithm for optimization of large structures on a cluster of workstations connected via a local area network (LAN).  ...  This paper presents distributed algo- rithms for optimization of large structures on a network of workstations using genetic algorithms.  ... 

Optimization Techniques Incorporating Evolutionary Model in Wireless Sensor Network: A Survey

Vidya Honguntikar, Dr. G. S Biradar
2014 IOSR Journal of Computer Engineering  
With several optimization algorithms existing to suit different problems, choosing a proper algorithm is very important in any optimization technique.  ...  Network optimization is a critical component and optimization techniques are used to achieve the design goals in Networking.  ...  data is propagated towards the sink.  Localization: For obtaining the accuracy in data reception and location, the complexity increases in large scale networks for complex environments.  ... 
doi:10.9790/0661-16521924 fatcat:pcvyy34mxvhydf7dw3hnwpwux4

Parallelizing and Analyzing the Behavior of Sequence Alignment Algorithm on a Cluster of Workstations for Large Datasets

Sathe S.R, Shrimankar D. D.
2013 International Journal of Computer Applications  
We propose the parallel implementation of the Wavefront algorithm based on a chunk size transformation to handle large dataset with message passing model.  ...  An MPI based parallelization technique for improving the scalability of the global sequence alignment algorithm on clusters of workstation is presented.  ...  Biologists are thus faced with the problem of dealing with large datasets, in the search of meaningful similarities among biological sequences [5] .  ... 
doi:10.5120/13042-0091 fatcat:k7xxybuwm5bbpagmovvjanxrkm

DFG Priority Programme SPP 1736: Algorithms for Big Data

Mahyar Behdju, Ulrich Meyer
2017 Künstliche Intelligenz  
In order to tackle those challenges, the German Research Foundation established in 2013 the priority programme SPP 1736: Algorithms for Big Data.  ...  In this article we give a short overview on the research topics represented within this priority programme.  ...  P10 Local Identification of Central Nodes, Clusters, and Network Motifs in Very Large Complex Networks K.  ... 
doi:10.1007/s13218-017-0518-4 fatcat:6vk5y3servb4bnos4lglbhziki

A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules

Anindya Bhattacharya, Yan Cui
2017 Scientific Reports  
Most biclustering algorithms use local search heuristics that may miss many biclusters.  ...  The selection of scoring function for the heuristic search is also important for finding the biologically meaningful biclusters.  ...  Funding: This work was partly supported by The University of Tennessee Center for Integrative and Translational Genomics. A.B. was supported in part by P-41-RR24851.  ... 
doi:10.1038/s41598-017-04070-4 pmid:28646174 pmcid:PMC5482832 fatcat:h5gxrfofqfempcjghwd2ou5k2q

Computational solutions to large-scale data management and analysis

Eric E. Schadt, Michael D. Linderman, Jon Sorenson, Lawrence Lee, Garry P. Nolan
2010 Nature reviews genetics  
Cloud-based computing The abstraction of the underlying hardware architectures (for example, servers, storage and networking) that enable convenient, on-demand network access to a shared pool of computing  ...  Many large computer systems now have many petabytes of storage.  ...  for computing large data sets is the parallelization of the analysis algorithms.  ... 
doi:10.1038/nrg2857 pmid:20717155 pmcid:PMC3124937 fatcat:a43tfxx6nzf4vg6rm5w44olgky
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