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Updated: May 22, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Foundation models in bioinformatics.

Fei Guo^1,2, Renchu Guan³, Yaohang Li⁴

¹Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.

National Science Review

|March 13, 2025

Summary

This summary is machine-generated.

Foundation models (FMs) are revolutionizing bioinformatics by efficiently analyzing large datasets for genomics, proteomics, and drug discovery. This review guides scientists in selecting FMs for computational biology advancements.

Keywords:

bioinformatics drug discovery foundation model genomics proteomics single-cell analysis transcriptomics

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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.