Evaluating the representational power of pre-trained DNA language models for regulatory genomics

Tang, Ziqi, Koo, Peter K (March 2024) Evaluating the representational power of pre-trained DNA language models for regulatory genomics. bioRxiv. (Public Dataset) (Submitted)

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Abstract

The emergence of genomic language models (gLMs) offers an unsupervised approach to learn a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experiments. Previous evaluations have shown pre-trained gLMs can be leveraged to improve prediction performance across a broad range of regulatory genomics tasks, albeit using relatively simple benchmark datasets and baseline models. Since the gLMs in these studies were tested upon fine-tuning their weights for each downstream task, determining whether gLM representations embody a foundational understanding of cis-regulatory biology remains an open question. Here we evaluate the representational power of pre-trained gLMs to predict and interpret cell-type-specific functional genomics data that span DNA and RNA regulation. Our findings suggest that current gLMs do not offer substantial advantages over conventional machine learning approaches that use one-hot encoded sequences. This work highlights a major limitation with current gLMs, raising potential issues in conventional pre-training strategies for the non-coding genome.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > quantitative biology bioinformatics > quantitative biology > quantitative genetics
CSHL Authors:	Tang, Ziqi Koo, Peter K
Communities:	CSHL labs > Koo Lab School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	4 March 2024
Date Deposited:	07 Mar 2024 15:12
Last Modified:	07 Mar 2024 15:12
Related URLs:	Publisher
Dataset ID:	https://github.com/amberT15/LLM_eval https://doi.org/10.5281/zenodo.8279716
URI:	https://repository.cshl.edu/id/eprint/41454

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