[Paper Review] Progress and Opportunities of Foundation Models in Bioinformatics

Qing Li, Zhihang Hu+6 more|arXiv (Cornell University)|Feb 6, 2024

Genetics, Bioinformatics, and Biomedical ResearchBiochemistry, Genetics and Molecular Biology3 citations

TL;DR

This survey reviews foundation models (FMs) in bioinformatics, detailing their evolution, methodologies, and applications in sequence analysis, structure prediction, function annotation, and multimodal integration. It highlights FMs' success in leveraging large-scale unlabeled biological data to overcome data scarcity and noise, while identifying challenges in explainability, bias, and data quality, and outlining future research directions for the field.

ABSTRACT

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly adept at handling large-scale, unlabeled data, a common scenario in biological contexts due to the time-consuming and costly nature of experimentally determining labeled data. This characteristic has allowed FMs to excel and achieve notable results in various downstream validation tasks, demonstrating their ability to represent diverse biological entities effectively. Undoubtedly, FMs have ushered in a new era in computational biology, especially in the realm of deep learning. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. We delve into the specifics of the problem at hand including sequence analysis, structure prediction, function annotation, and multimodal integration, comparing the structures and advancements against traditional methods. Furthermore, the review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases. Finally, we outline potential development paths and strategies for FMs in future biological research, setting the stage for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also as a roadmap for future explorations and applications of FMs in biology.

Motivation & Objective

• To systematically investigate the evolution and current state of foundation models (FMs) in bioinformatics.
• To analyze the application of FMs across key biological tasks, including sequence analysis, protein structure prediction, function annotation, and multimodal data integration.
• To compare FM-based approaches with traditional methods in terms of performance, scalability, and adaptability.
• To identify critical challenges such as data noise, model interpretability, and potential biases in FM deployment for biological research.
• To outline future development paths and strategic opportunities for advancing FM applications in computational biology.

Proposed method

• The paper conducts a comprehensive survey of recent advances in foundation models (FMs) applied to bioinformatics, focusing on self-supervised and contrastive pretraining on large-scale unlabeled biological sequences.
• It evaluates FM architectures such as transformer-based models and contrastive learning frameworks tailored for biological sequences and structures.
• The review compares FM performance against classical machine learning and traditional deep learning models across downstream tasks like protein function prediction and structure modeling.
• It analyzes architectural choices, pretraining objectives, and fine-tuning strategies used in state-of-the-art FMs in bioinformatics.
• The study incorporates qualitative and quantitative assessments of model robustness, generalization, and interpretability across diverse biological datasets.
• It synthesizes insights from 27 pages of analysis, 3 figures, and 2 tables to map the current landscape and future trajectories of FMs in biology.

Experimental results

Research questions

• RQ1 How have foundation models transformed the landscape of bioinformatics in handling large-scale, unlabeled biological data?
• RQ2 What are the key architectural and training methodologies enabling FMs to outperform traditional models in sequence and structure prediction tasks?
• RQ3 In what ways do FMs improve function annotation and multimodal integration in biological systems?
• RQ4 What are the primary limitations of FMs in bioinformatics, particularly regarding data noise, model explainability, and bias?
• RQ5 What strategic research directions can further enhance the reliability and applicability of FMs in biological discovery?

Key findings

• Foundation models have significantly advanced bioinformatics by effectively leveraging large-scale unlabeled biological data, overcoming historical bottlenecks related to data scarcity and labeling costs.
• FMs demonstrate strong performance in downstream tasks such as protein structure prediction and function annotation, often outperforming traditional supervised models.
• Self-supervised pretraining on massive biological sequences enables FMs to learn generalizable representations across diverse biological entities and modalities.
• Despite their success, FMs face challenges in interpretability, susceptibility to data noise, and potential biases from skewed or non-representative training data.
• The review identifies multimodal integration—especially combining genomics, proteomics, and structural data—as a key frontier for future FM development.
• The paper outlines a roadmap for future research, emphasizing the need for robust, interpretable, and biologically grounded foundation models in computational biology.