Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction.
Antibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-base...
Saved in:
| Main Authors: | Meng Wang, Jonathan Patsenker, Henry Li, Yuval Kluger, Steven H Kleinstein |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-03-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012153 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Applying SoftTriple Loss for Supervised Language Model Fine Tuning
by: Witold Sosnowski, et al.
Published: (2022-09-01) -
Fine-Tuning Pre-Trained Large Language Models for Price Prediction on Network Freight Platforms
by: Pengfei Lu, et al.
Published: (2025-08-01) -
TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models
by: Weichuan Mo, et al.
Published: (2025-01-01) -
Enhancing Health Mention Classification Through Reexamining Misclassified Samples and Robust Fine-Tuning Pre-Trained Language Models
by: Deyu Meng, et al.
Published: (2024-01-01) -
Accelerated diffusion tensor imaging with self-supervision and fine-tuning
by: Phillip Martin, et al.
Published: (2025-04-01)