Demo Data from EDBT 2026 / reviewers in the wild / expert
Adrien Mialland
dblp:343/0594
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0001-5359-674XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author · 2 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 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics
variant effect prediction |
1.0 | 1 | 2026 | Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
protein language model embeddings · 1.0data augmentation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Fitness translocation: improving variant effect prediction with biologically-grounded data augmentationabstractMOTIVATION: Data scarcity limits the characterization of protein fitness landscapes and the development of accurate variant effect prediction models. To address this challenge, we introduce fitness translocation, a data augmentation strategy that generates synthetic variants for a target protein by leveraging variant fitness data previously measured in homologous proteins. Using embeddings from protein language models, the method computes the difference between each homolog variant and its wild type and applies these offsets to the target wild-type embedding to create synthetic variants in embedding space. RESULTS: We illustrate the utility of fitness translocation in the context of variant effect prediction on three protein families: IGPS, GFP, and SARS-CoV-2 spike proteins, across different models and training data sizes. Fitness translocation consistently improves predictive performance, particularly under limited training data, and is effective even when augmenting with remote homologs sharing as little as 35% sequence identity. These results illustrate how biologically grounded data augmentation can expand and diversify protein fitness landscapes, supporting more data-efficient protein engineering. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available at https://github.com/adrienmialland/ProtFitTrans. Adrien Mialland, Shuzo Fukunaga, Riku Katsuki, Yunfei Dong, Hideki Yamaguchi, Yutaka Saito |
Bioinform. | 1 |
| 2023 | Stylohyoid and posterior digastric timing evaluationabstractIn the context of real-time and safe indwelling detection of swallowing, which would enable the conception of an implantable active artificial larynx, we evaluate the timing of the stylohyoid and the posterior digastric muscles. They were shown to activate at the beginning of swallowing, both from human and animal results, but only with an indirect imaging method on humans. Therefore, this paper analyses their onset, offset, and peak time through the first method, that we previously set up, which allowed their direct functional analysis with intramuscular electromyography, surface electromyography, and swallowing sound. We found that both muscle activate at the beginning of swallowing, are relatively stable, while the posterior digastric duration were the shortest. Adrien Mialland, Ihab Atallah, Agnès Bonvilain |
BSN | 1 |