Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuanqing Zhou
dblp:384/4164
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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% | |
| Artificial intelligence
1 paper |
Generative modeling · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein design
antibody affinity maturation |
0.8 | 1 | 2024 | GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024 |
Bioinformatics and computational biology › protein design
antibody design |
0.8 | 1 | 2024 | GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024 |
Bioinformatics and computational biology
protein design |
0.8 | 1 | 2024 | GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024 |
Bioinformatics and computational biology
protein engineering |
0.8 | 1 | 2024 | GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024 |
Methods — techniques the papers use, named apart from their topics
position refinement · 1.5mutation effect prediction · 1.5generative geometry initialization · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | GeoAB: Towards Realistic Antibody Design and Reliable Affinity MaturationabstractIncreasing works for antibody design are emerging to generate sequences and structures in Complementarity Determining Regions (CDRs), but problems still exist. We focus on two of them: (i) authenticity of the generated structure and (ii) rationality of the affinity maturation, and propose GeoAB as a solution. In specific, GeoAB-Designergenerates CDR structures with realistic internal geometries, composed of a generative geometry initializer (Geo-Initializer) and a position refiner (Geo-Refiner); GeoAB-Optimizer achieves affinity maturation by accurately predicting both the mutation effects and structures of mutant antibodies with the same network architecture as Geo-Refiner. Experiments show that GeoAB achieves state-of-the-art performance in CDR co-design and mutation effect predictions, and fulfills the discussed tasks effectively. Lirong Wu, Yufei Huang 0002, Yunfan Liu 0002, Odin Zhang, Yuanqing Zhou, Stan Z. Li |
ICML | 6 |