Table 5_Utilizing machine learning and bioinformatics analysis to identify drought stress responsive genes in wheat (Triticum aestivum L.).xlsx

One of the main abiotic stressors affecting agricultural output is drought stress, which has a substantial impact on wheat growth, development, and yield. This study aims to uncover transcriptomic changes in wheat leaves under drought stress using machine learning and bioinformatics approaches, thereby providing new research perspectives and solutions for understanding the mechanisms of abiotic stress responses in wheat and identifying drought-tolerant genes. First, publicly available RNA sequencing data on wheat drought stress were retrieved from databases, followed by sequence alignment and quantitative expression analysis. Differentially expressed genes (DEGs) under drought stress were identified through differential expression analysis. Subsequently, a weighted gene co-expression network was constructed to determine key gene modules, and multiple machine learning models were compared for their performance. Finally, an improved Random Forest-Boruta (RF-Boruta) algorithm was employed to identify key genes closely associated with drought stress responses. The differential expression analysis identified 16,754 DEGs, and the constructed gene co-expression network successfully identified modules related to drought stress responses. Among the various machine learning models, the random forest algorithm performed best in identifying drought stress-responsive genes. The improved RF-Boruta algorithm further selected candidate genes highly related to drought stress, improving model accuracy from 0.889 to 0.942 and the area under the curve (AUC) from 0.968 to 0.978. Gene enrichment analysis was also conducted. By integrating bioinformatics and machine learning techniques, this study identified key genes highly associated with drought stress responses in wheat, providing important insights into the potential mechanisms of drought responses in wheat.