EDBT 2026 Demo / reviewers in the wild / expert
Kamil Pawlicki
dblp:359/2938
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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% |
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 › sequence analysis
homology detection |
0.7 | 1 | 2023 | pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models · Bioinform. 2023 |
Bioinformatics and computational biology
protein sequence analysis |
0.7 | 1 | 2023 | pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models · Bioinform. 2023 |
Bioinformatics and computational biology › sequence analysis › homology detection
remote homology detection |
0.7 | 1 | 2023 | pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models · Bioinform. 2023 |
Bioinformatics and computational biology › sequence analysis › sequence feature extraction
sequence embedding |
0.2 | 1 | 2023 | pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
protein language model · 0.7local alignment · 0.7embedding comparison · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language modelsabstractMOTIVATION: The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task. RESULTS: We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation. AVAILABILITY AND IMPLEMENTATION: pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed databases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performing batch searches (https://github.com/labstructbioinf/pLM-BLAST). Kamil Kaminski, Jan Ludwiczak, Kamil Pawlicki, Vikram Alva, Stanislaw Dunin-Horkawicz |
Bioinform. | 3 |