Demo Data from EDBT 2026 / reviewers in the wild / expert
Craig McAnulla
dblp:77/2215
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
functional genomics |
0.2 | 1 | 2014 | InterProScan 5: genome-scale protein function classification · Bioinform. 2014 |
Bioinformatics and computational biology › protein function prediction
protein classification |
0.2 | 1 | 2014 | InterProScan 5: genome-scale protein function classification · Bioinform. 2014 |
Bioinformatics and computational biology
protein function prediction |
0.2 | 1 | 2014 | InterProScan 5: genome-scale protein function classification · Bioinform. 2014 |
High-performance computing
cluster computing |
0.1 | 1 | 2014 | InterProScan 5: genome-scale protein function classification · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
distributed data analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | InterProScan 5: genome-scale protein function classificationabstractAbstract 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. | 6 |
| 2012 | Metagenomic analysis: the challenge of the data bonanzaabstractSeveral thousand metagenomes have already been sequenced, and this number is set to grow rapidly in the forthcoming years as the uptake of high-throughput sequencing technologies continues. Hand-in-hand with this data bonanza comes the computationally overwhelming task of analysis. Herein, we describe some of the bioinformatic approaches currently used by metagenomics researchers to analyze their data, the issues they face and the steps that could be taken to help overcome these challenges. Christopher I. Hunter, Alex L. Mitchell, Philip Jones, Craig McAnulla, Sebastien Pesseat, Maxim Scheremetjew, Sarah Hunter |
Briefings Bioinform. | 4 |