Demo Data from EDBT 2026 / reviewers in the wild / expert
Lewis Chinery
dblp:370/4481
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-9222-185XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › immunoinformatics
paratope prediction |
0.7 | 1 | 2023 | Paragraph - antibody paratope prediction using graph neural networks with minimal feature vectors · Bioinform. 2023 |
Bioinformatics and computational biology
protein structure prediction |
0.7 | 1 | 2023 | Paragraph - antibody paratope prediction using graph neural networks with minimal feature vectors · Bioinform. 2023 |
Bioinformatics and computational biology › structural bioinformatics
structure-based prediction |
0.7 | 1 | 2023 | Paragraph - antibody paratope prediction using graph neural networks with minimal feature vectors · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
graph neural network · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Paragraph - antibody paratope prediction using graph neural networks with minimal feature vectorsabstractSUMMARY: The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by improving our understanding of antibody-antigen binding. We present Paragraph, a structure-based paratope prediction tool that outperforms current state-of-the-art tools using simpler feature vectors and no antigen information. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at www.github.com/oxpig/Paragraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Lewis Chinery, Newton Wahome, Iain H. Moal, Charlotte M. Deane |
Bioinform. | 1 |