Demo Data from EDBT 2026 / reviewers in the wild / expert
Paul W. Fitzjohn
dblp:21/2156
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure prediction
protein structure refinement |
0.1 | 1 | 2006 | Developing a move-set for protein model refinement · Bioinform. 2006 |
Bioinformatics and computational biology
structural bioinformatics |
0.1 | 1 | 2006 | Developing a move-set for protein model refinement · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
genetic algorithm · 0.1cost function · 0.1conformational search · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Developing a move-set for protein model refinementabstractMOTIVATION: A wide variety of methods for the construction of an atomic model for a given amino acid sequence are known, the more accurate being those that use experimentally determined structures as templates. However, far fewer methods are aimed at refining these models. The approach presented here carefully blends models created by several different means, in an attempt to combine the good quality regions from each into a final, more refined, model. RESULTS: We describe here a number of refinement operators (collectively, 'move-set') that enable a relatively large region of conformational space to be searched. This is used within a genetic algorithm that reshuffles and repacks structural components. The utility of the move-set is demonstrated by introducing a cost function, containing both physical and other components guiding the input structures towards the target structure. We show that our move-set has the potential to improve the conformation of models and that this improvement can be beyond even the best template for some comparative modelling targets. AVAILABILITY: The populus software package and the source code are available at http://bmm.cancerresearchuk.org/~offman01/populus.html. Marc N. Offman, Paul W. Fitzjohn, Paul A. Bates |
Bioinform. | 2 |