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Siew-Yit Yong

dblp:01/11251 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
0000-0002-1952-3528ORCID · 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
High-performance computing · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
functional genomics
0.212014
InterProScan 5: genome-scale protein function classification · Bioinform. 2014
Bioinformatics and computational biology › protein function prediction
protein classification
0.212014
InterProScan 5: genome-scale protein function classification · Bioinform. 2014
Bioinformatics and computational biology
protein function prediction
0.212014
InterProScan 5: genome-scale protein function classification · Bioinform. 2014
High-performance computing
cluster computing
0.112014
InterProScan 5: genome-scale protein function classification · Bioinform. 2014

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

distributed data analysis · 0.4
YearPublicationVenuePosition
2014 InterProScan 5: genome-scale protein function classification
abstract
Abstract Motivation: Robust large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterize many millions of sequences. Here, we describe a new Java-based architecture for the widely used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete reimplementation of the software framework, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the open source code is hosted at Google Code. Availability and implementation: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google.com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or [email protected] or [email protected]
Philip Jones, David Binns, Hsin-Yu Chang, Matthew Fraser, Weizhong Li 0001, Craig McAnulla, Hamish McWilliam, John Maslen, Alex L. Mitchell, Gift Nuka, Sebastien Pesseat, Antony F. Quinn, Amaia Sangrador-Vegas, Maxim Scheremetjew, Siew-Yit Yong, Rodrigo Lopez, Sarah Hunter
Bioinform.15