Peer-reviewedLicence not statedNature Computational Science
Understanding language model scaling for protein fitness prediction
6 affiliations
- 1Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. [email protected]
- 2Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- 3Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA. [email protected]
- 4Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. [email protected]
- 5JP Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA. [email protected]
- 6Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY, USA. [email protected]
Related
- Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks
- Protein Language Model-Boosted Prediction of Isoelectric Point
- Protein–Protein Interface Residue Prediction by Integrating Protein Language Representations and Structural Graph Attention
- AIPFusion-Net: an interaction-fusion framework for anti-inflammatory peptide prediction using protein language model embeddings, local sequence patterns, and handcrafted descriptors.
- ProteinJEPA: Latent prediction improves protein language model pretraining
BibTeX
@article{hou2026understanding,
title = {{Understanding language model scaling for protein fitness prediction}},
author = {Chao Hou and Ди Лю and Aziz Zafar and Yufeng Shen},
journal = {Nature Computational Science},
year = {2026},
month = jul,
doi = {10.1038/s43588-026-01010-z},
url = {https://doi.org/10.1101/2025.04.25.650688},
}
RIS
TY - JOUR
TI - Understanding language model scaling for protein fitness prediction
AU - Chao Hou
AU - Ди Лю
AU - Aziz Zafar
AU - Yufeng Shen
PY - 2026
DA - 2026/07/13
JO - Nature Computational Science
DO - 10.1038/s43588-026-01010-z
UR - https://doi.org/10.1101/2025.04.25.650688
SN - 2662-8457
ER -