Improving Genomic Models via Task-Specific Self-Pretraining
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913906602016768 |
|---|---|
| author | Mupparapu, Sohan Krishnamurthy, Parameswari Puduppully, Ratish |
| author_facet | Mupparapu, Sohan Krishnamurthy, Parameswari Puduppully, Ratish |
| contents | Pretraining DNA language models (DNALMs) on the full human genome is resource-intensive, yet often considered necessary for strong downstream performance. Inspired by recent findings in NLP and long-context modeling, we explore an alternative: self-pretraining on task-specific, unlabeled data. Using the BEND benchmark, we show that DNALMs trained with self-pretraining match or exceed the performance of models trained from scratch under identical compute. While genome-scale pretraining may still offer higher absolute performance, task-specific self-pretraining provides a practical and compute-efficient strategy for building stronger supervised baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Genomic Models via Task-Specific Self-Pretraining Mupparapu, Sohan Krishnamurthy, Parameswari Puduppully, Ratish Genomics Pretraining DNA language models (DNALMs) on the full human genome is resource-intensive, yet often considered necessary for strong downstream performance. Inspired by recent findings in NLP and long-context modeling, we explore an alternative: self-pretraining on task-specific, unlabeled data. Using the BEND benchmark, we show that DNALMs trained with self-pretraining match or exceed the performance of models trained from scratch under identical compute. While genome-scale pretraining may still offer higher absolute performance, task-specific self-pretraining provides a practical and compute-efficient strategy for building stronger supervised baselines. |
| title | Improving Genomic Models via Task-Specific Self-Pretraining |
| topic | Genomics |
| url | https://arxiv.org/abs/2506.17766 |