Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
(2402.08075)Abstract
Although DNA foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. This limitation starkly contrasts with the success of natural language foundation models, which thrive on substantially larger scales. Furthermore, genome understanding involves numerous downstream genome annotation tasks with inherent data heterogeneity, thereby necessitating more efficient and robust fine-tuning methods tailored for genomics. Here, we present \textsc{Lingo}: \textsc{L}anguage prefix f\textsc{In}e-tuning for \textsc{G}en\textsc{O}mes. Unlike DNA foundation models, \textsc{Lingo} strategically leverages natural language foundation models' contextual cues, recalibrating their linguistic knowledge to genomic sequences. \textsc{Lingo} further accommodates numerous, heterogeneous downstream fine-tune tasks by an adaptive rank sampling method that prunes and stochastically reintroduces pruned singular vectors within small computational budgets. Adaptive rank sampling outperformed existing fine-tuning methods on all benchmarked 14 genome understanding tasks, while requiring fewer than 2\% of trainable parameters as genomic-specific adapters. Impressively, applying these adapters on natural language foundation models matched or even exceeded the performance of DNA foundation models. \textsc{Lingo} presents a new paradigm of efficient and scalable genome understanding via genomic-specific adapters on language models.
Overview
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Leveraging pre-trained natural language models (PLMs) for genomic sequence analysis introduces a novel approach for interpreting complex biological data, but requires adaptive fine-tuning due to the differences between human language and genetic data structures.
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The study introduces 'Lingo', a framework that optimizes PLMs for genomic data analysis through an advanced fine-tuning method, using byte-level byte-pair encoding (BBPE) for efficient genomic sequence tokenization.
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Lingo employs an adaptive rank sampling technique to enhance its capability to handle the diversity of genomic data, demonstrating superior performance on genomic understanding tasks compared to traditional models and methods.
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The research highlights Lingo's potential as a scalable solution for genomic data analysis, paving the way for future exploration in computational biology and the broader application of PLMs beyond traditional NLP tasks.
Adaptive Fine-Tuning of Pre-trained Language Models for Genomic Data Interpretation
Introduction to Efficient Genomic Sequence Analysis
Leveraging pre-trained natural language models (PLMs) in genomic sequence analysis represents a novel strategy for understanding complex biological data. Recent advancements have underscored the efficacy of deploying LLMs like GPT-3 for a wide spectrum of applications beyond traditional NLP tasks. However, directly applying these PLMs to genomic sequences presents unique challenges, owing to the intrinsic differences between human language syntax/semantics and genetic data's structure. This disparity necessitates refining the application of PLMs to ensure effective interpretation and analysis of genomic sequences.
Lingo: Bridging Linguistics and Genomics
In response to the aforementioned challenges, the study introduces Lingo - a methodical framework optimizing PLMs for genomic data analysis through an innovative fine-tuning approach named Language prefix fIne-tuning for GenOmes. Diverging from typical DNA foundation models, Lingo ingeniously applies linguistic knowledge, encapsulated within PLMs, to the domain of genomics. Through strategic modification and application of byte-level byte-pair encoding (BBPE) for genomic sequence tokenization, Lingo efficiently adapts PLMs to interpret genetic data, thereby significantly improving upon the existing groundwork laid by models such as DNABERT and the Nucleotide Transformer in terms of scalability and efficiency.
Methodology and Technical Innovations
Central to Lingo’s approach is the adaptive rank sampling technique, designed to address the inherent heterogeneity of genomic data. This method selectively prunes and reintroduces singular vectors based on their relevance, operating within a constrained cubic budget schedule. Such a strategy not only enhances the model's capability to cope with the diverse and complex nature of genomic sequences but also significantly reduces the computational overhead associated with traditional full-model fine-tuning techniques. By applying BBPE tokenization, Lingo further refines the model's ability to recognize and process the recurrent patterns in DNA sequences efficiently.
Experimental Insights and Comparative Analysis
The empirical evaluation of Lingo across several genome understanding tasks showcases its superior performance relative to both the traditional DNA foundation models and alternate parameter-efficient fine-tuning (PEFT) methods. Notably, when applied to OPT models, Lingo achieves commendable results on 14 benchmark genomic sequence datasets, outperforming state-of-the-art DNA foundation models in terms of efficiency and scalability while requiring a fraction of the trainable parameters. These metrics underline Lingo's potential as a robust and scalable solution for genomic data analysis.
Future Directions and Theoretical Implications
The integration of Lingo with PLMs opens new avenues for computational biology, highlighting the potential of cross-disciplinary applications of LLMs in scientific research. The approach signifies a step towards more efficient and scalable methodologies for genomic analysis, capable of accommodating the vast and varied data typical of genome-related tasks. Future explorations may delve into further optimizations and applications of Lingo, potentially enhancing our understanding of genetic structures and functions. The theoretical implications of this research also prompt a reevaluation of foundational model adaptability across different domains, suggesting a broader applicability of PLMs beyond traditional text-based tasks.
Conclusion
The study presents Lingo as a pioneering framework that effectively adapts PLMs for genomic sequence interpretation. Through adaptive rank sampling and BBPE tokenization, Lingo not only demonstrates remarkable efficiency and scalability but also sets a precedent for future implementations of foundational language models in computational genomics. This approach leverages the vast knowledge encapsulated in PLMs, signaling a promising pathway for advancing genome understanding and analysis.
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