Foundation models in bioinformatics

Area of Science:

• Bioinformatics
• Computational Biology
• Artificial Intelligence

Background:

• Foundation models (FMs) are increasingly vital in bioinformatics, addressing challenges in pre-training, evaluation, and interpretability.
• FMs excel at handling large, unlabeled biological datasets, overcoming the limitations of costly experimental procedures.
• These models demonstrate high accuracy in representing biological entities, driving innovation in computational biology.

Purpose of the Study:

• To review recent advancements in foundation models (FMs) applied to bioinformatics.
• To guide scientists in selecting appropriate FMs for diverse bioinformatics tasks.
• To highlight the role of AI in advancing molecular biology and understanding molecular landscapes.

Main Methods:

• Review of recent literature on foundation models in bioinformatics.
• Categorization of FMs into language, vision, graph, and multimodal types.
• Analysis of FM applications across genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis.

Main Results:

• FMs have shown significant success in various downstream bioinformatics tasks.
• Demonstrated high accuracy in biological entity representation and large dataset management.
• Identified four main types of FMs applicable to bioinformatics challenges.

Conclusions:

• Foundation models represent a new era in computational biology, offering powerful tools for scientific discovery.
• AI, through FMs, provides a foundation for continued innovation in molecular biology.
• Effective selection of FMs is crucial for maximizing their impact in bioinformatics research.