# PREFER: An Ontology for the PREcision FERmentation Community

arXiv: https://arxiv.org/abs/2602.16755 · HTML: https://mlsanity.com/paper/2602.16755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies](https://mlsanity.com/paper/2207.02056/) (2022)
- [LinkML: An Open Data Modeling Framework](https://mlsanity.com/paper/2511.16935/) (2025)
- [A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives](https://mlsanity.com/paper/2503.18213/) (2025)
- [OLS4: A new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem](https://mlsanity.com/paper/2501.13034/) (2025)
- [Digital Evolution: Novo Nordisk's Shift to Ontology-Based Data Management](https://mlsanity.com/paper/2405.05413/) (2024)
- [The Cell Ontology in the age of single-cell omics](https://mlsanity.com/paper/2506.10037/) (2025)

## Abstract (arXiv)

Precision fermentation relies on microbial cell factories to produce sustainable food, pharmaceuticals, chemicals, and biofuels. Specialized laboratories such as biofoundries are advancing these processes using high-throughput bioreactor platforms, which generate vast datasets. However, the lack of community standards limits data accessibility and interoperability, preventing integration across platforms. In order to address this, we introduce PREFER, an open-source ontology designed to establish a unified standard for bioprocess data. Built in alignment with the widely adopted Basic Formal Ontology (BFO) and connecting with several other community ontologies, PREFER ensures consistency and cross-domain compatibility and covers the whole precision fermentation process. Integrating PREFER into high-throughput bioprocess development workflows enables structured metadata that supports automated cross-platform execution and high-fidelity data capture. Furthermore, PREFER's standardization has the potential to bridge disparate data silos, generating machine-actionable datasets critical for training predictive, robust machine learning models in synthetic biology. This work provides the foundation for scalable, interoperable bioprocess systems and supports the transition toward more data-driven bioproduction.