Machine learning for multi-omics data integration in crop improvement: a systematic review

Alemu Tsega; Destaw Mullualem

doi:10.1186/s12859-026-06438-8

April 1, 2026BMC BioinformaticsOpen Access

Machine learning for multi-omics data integration in crop improvement: a systematic review

View Full Paper

Ask AI

Bookmark

Share

Key Points

The review aims to synthesize applications of machine learning for integrating multi-omics data in crop improvement.
Conducted a systematic review of 76 eligible studies.
Examined methodologies for integrating environmental data with omics layers.
Analyzed the performance of various machine learning models used for crop prediction.
Tree-based models like random forest and XGBoost were most common due to their interpretability.
Deep learning models performed well on larger datasets but had higher computational costs.
Feature-engineering hybrids achieved accuracy gains of 10-15% in predicting complex traits.

No abstract yet

We only have this paper's title and publication details.

Read it at the source

Implication

Systematic review highlights ML's role in enhancing crop improvement through multi-omics data integration, suggesting transformative potential for agriculture.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Tsega et al. (2026) studied this question.

synapsesocial.com/papers/69cf5d4e5a333a821460b020 https://doi.org/10.1186/s12859-026-06438-8

View Full Paper

Ask AI

Bookmark

Share

Machine learning for multi-omics data integration in crop improvement: a systematic review

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Machine learning for multi-omics data integration in crop improvement: a systematic review

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Keep reading

Keep reading