# Challenges and opportunities: computational biology and the future of agriculture

**Authors:** Joao Carlos Gomes-Neto, Alexandra Crook, Rachel Hestrin, Guoming Li, Chia-Sin Liew, Guilherme Rosa, Keshav D Singh, Christopher K Tuggle, Katie L Summers, Camilo Valdes, Noah Fahlgren, Jennifer Clarke

PMC · DOI: 10.1093/bioadv/vbag003 · Bioinformatics Advances · 2026-02-03

## TL;DR

This paper discusses how computational biology can help address agricultural challenges through innovations like AI and data standards.

## Contribution

The paper highlights new opportunities and challenges in applying computational biology to agriculture, emphasizing collaboration and sustainability.

## Key findings

- Experts discussed topics like genomics and AI in agriculture.
- Barriers include data sharing and the need for FAIR data standards.
- Collaboration and new skills are needed for impactful work in the field.

## Abstract

The world of agriculture is rapidly changing with advances in artificial intelligence and demands for greater feed and food security considering environmental and sustainability challenges. The 30th Conference on Intelligent Systems in Molecular Biology (ISMB) held in July 2022 featured an invited session on the role of computational biology in Digital and Precision Agriculture. This session featured presentations by experts from various subdisciplines on novel research discoveries and a panel discussion on Digital Agriculture at Scale. Topics discussed during the session included genetics, epigenetics, and genomics of agriculturally relevant species; foodborne pathogen genomics and epidemiology; plant and animal phenomics; AI/machine learning; image analysis; remote sensing; educational innovations; discoveries resulting from public-private partnerships; data sharing and findable, accessible, interoperable, and reproducible (FAIR) data standards; biotechnology; and soil microbial ecology and biogeochemistry.

We present several of the current and future challenges and opportunities for computational biology in agriculture including why these challenges are important to address, what barriers exist, and what skills and competencies are required to be successful as a computational biologist in agriculture. We intend this summary to engage the computational biology community and attract them to the opportunities available for interesting and impactful work toward ensuring sustainable food security.

## Full-text entities

- **Diseases:** foodborne (MESH:D005517), COVID (MESH:D000086382), DA (MESH:C000721267)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Mutations:** 8042-31440-002-00D to K

## Full text

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## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12916170/full.md

## References

23 references — full list in the complete paper: https://tomesphere.com/paper/PMC12916170/full.md

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Source: https://tomesphere.com/paper/PMC12916170