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Related Experiment Video

Updated: Jan 15, 2026

Scalable Transfection of Maize Mesophyll Protoplasts

Scalable Transfection of Maize Mesophyll Protoplasts

Published on: June 23, 2023

Delivering artificial intelligence-ready genomics with the Maize Genetics and Genomics Database.

Olivia C Haley¹, Laura E Tibbs-Cortes¹, Stephen F Harding¹

¹USDA Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, 819 Wallace Rd., Ames, IA 50011, United States.

|January 14, 2026

Summary

This summary is machine-generated.

MaizeGDB is preparing maize data for Artificial Intelligence (AI) integration, enabling faster discovery of gene function and improved crop varieties. This AI-ready infrastructure supports researchers in agricultural biotechnology.

Keywords:

Zea mays FAIR data artificial intelligence bioinformatics feature store genomics language models machine learning maize model organism database

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Agrobacterium-Mediated Immature Embryo Transformation of Recalcitrant Maize Inbred Lines Using Morphogenic Genes

Agrobacterium-Mediated Immature Embryo Transformation of Recalcitrant Maize Inbred Lines Using Morphogenic Genes

Published on: February 14, 2020

Area of Science:

Computational biology
Agricultural biotechnology
Genomics

Background:

Artificial Intelligence (AI) integration is transforming biological research, especially in agriculture.
Maize (Zea mays L.) possesses extensive genomic and genetic resources, making it ideal for AI-driven crop improvement.
Large, complex datasets in maize research present opportunities for AI-based discovery.

Purpose of the Study:

To detail MaizeGDB's strategic initiatives in creating an AI-ready infrastructure for maize data.
To enable the maize research and breeding community to leverage AI for accelerated discovery.
To standardize datasets and generate precomputed embeddings using DNA and protein language models.

Main Methods:

Standardizing maize datasets and pre-computing complex features.
Developing novel interactive tools and reproducible workflows.
Generating precomputed embeddings from DNA and protein language models for AI applications.

Main Results:

MaizeGDB has established an AI-ready data foundation in standardized formats.
New functionalities include zero-shot variant effect scoring and visualization of nucleotide conservation.
Custom dataset assembly resources and reproducible workflows are available via GitHub.

Conclusions:

MaizeGDB's AI-ready infrastructure facilitates AI integration in maize research.
This initiative accelerates the discovery of gene function and variant interpretation in maize.
The resources provided will aid in the development of improved maize varieties through AI.