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Allan B. Clark

dblp:38/4 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0003-2965-8941ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 50% Cloud and datacenter computing · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › systems biology
parameter estimation
0.212013
SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology · Bioinform. 2013
Bioinformatics and computational biology
systems biology
0.212013
SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology · Bioinform. 2013
Distributed systems › middleware
distributed computing middleware
0.212013
SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology · Bioinform. 2013
Cloud and datacenter computing
job scheduling
0.212013
SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology · Bioinform. 2013

Methods — techniques the papers use, named apart from their topics

plugin architecture · 0.3parallel optimization algorithms · 0.3
YearPublicationVenuePosition
2013 SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology
abstract
SUMMARY: Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats. AVAILABILITY AND IMPLEMENTATION: All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.
Richard R. Adams, Allan B. Clark, Azusa Yamaguchi, Neil Hanlon, Nikos Tsorman, Shakir Ali, Galina Lebedeva, Alexey N. Goltsov, Anatoly A. Sorokin, Ozgur E. Akman, Carl Troein, Andrew J. Millar, Igor Goryanin, Stephen Gilmore
Bioinform.2