Machine-learning based biomarker discovery for Striga resistance in sorghum

Sorghum bicolor is the fifth most important cereal crop in the world, cultivated across the globe in almost ۱۱۰ countries, predominantly in Asia and Africa but also in Europe, America, and Oceania. Despite its outstanding resilience to abiotic stresses, approximately ۲۰% of sorghum yield is annually lost due to infestation with the parasitic weed Striga hermonthica. Identifying sources of Striga resistance gene within sorghum is imperative to developing resistant sorghum cultivars. Feature selection algorithms are frequently employed in preprocessing machine learning pipelines applied to biological data to identify relevant features. The objective of this study is about selecting the important features along with improving the prediction accuracy. Therefore, we propose to use an integrated strategy including Information gain, Gain Ratio, and ReliefF to filter important genes involved in striga resistance in sorghum.

کلیدواژه ها:

Striga hermonthica ، information gain ، gain ratio ، ReliefF