Unveiling insights into drought-tolerant responses in soybean: a data-driven pipeline for feature engineering in biomarker discovery

Abstract

Soybean [Glycine max (L.) Merr.] is an important global food crop but is highly vulnerable to environmental changes, particularly drought. Conventional strategies of biomarkers discovery for developing drought-tolerant varieties are resource-intensive, inefficient, and without comprehensive insight. The current study proposed a novel data-driven pipeline for feature engineering through integrating diverse genetic data from multidisciplinary research on cloud-based sources in identification of key drought-tolerant genes (DTgenes) in soybean. Our pipeline involved data extraction, transformation, loading, and systematic integration of both omics and non-omics data. Feature prioritization was performed for feature selection to uncover the important biomarkers from feature pool (candidate gene pool), and key DTgenes were identified through integrative systems biology strategies based on the biomarker candidates. The validation of identified key DTgenes was carried out by both computational and molecular experiments approach. Both approaches demonstrated the credibility and potential of the key DTgenes for conferring drought tolerance response in soybean. This data-driven pipeline for feature engineering approach enhances the efficiency and accuracy of biomarkers discovery for further breeding program, which uncover a robust key DTgenes candidate which contribute to drought tolerance improvement in soybean and show potential of applicability for other crop species.