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arXiv · 2506.17766

Improving Genomic Models via Task-Specific Self-Pretraining

Sohan Mupparapu·Parameswari Krishnamurthy·Ratish Puduppully
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Abstract

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.

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Sohan Mupparapu, Parameswari Krishnamurthy, Ratish Puduppully. 2025-06-21. Improving Genomic Models via Task-Specific Self-Pretraining. https://arxiv.org/abs/2506.17766

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