aceBenchmarking ensemble and deep learning models for drought-responsive gene prioritisation in rice using multi-omics data
Identifying drought-responsive genes is critical for climate-resilient crop breeding, yet computational gene-trait association often relies on single-omics or generalized genomic datasets. Optimal algorithm selection for high-dimensional, small multi-omics datasets also remains a persistent challenge. We present a machine learning framework that extends the QTG-Finder feature set by integrating transcriptomic and proteomic features to classify drought-responsive genes in rice. Using MBKBase annotations, we evalu…
Adding transcriptomic and proteomic features to a genomics-based classifier raised recall for identifying drought-responsive rice genes from 0.47 to 0.67 in a small multi-omics dataset.
The reported performance is conditional on a feature matrix assembled before gene-level data splitting, and the 10-gene internal holdout is too small to support claims of generalizability.
Evidence
- Peer-reviewedPLOS One2026-10-09
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Truvace Impact Record TRV-2026-1675, v1: “Benchmarking ensemble and deep learning models for drought-responsive gene prioritisation in rice using multi-omics data.” Truvace, 2026-10-17. /record/TRV-2026-1675 (accessed at citation time). sha256 c95b741568fbf4ad…
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