Publications by authors named "Maxim S Kovalev"
A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model.
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- Deleterious mutations in plants are not fully understood in terms of their impact on fitness and crop productivity, largely due to insufficient mutational datasets.
- A novel machine learning classifier was developed using 18 features to distinguish deleterious mutations from neutral ones, with Random Forest showing the best accuracy compared to other methods and the PolyPhen-2 tool.
- The classifier demonstrated high accuracy in predicting deleterious mutations across different plant species and has the potential to enhance the identification of functional mutations, aiding in breeding improvements and the creation of new cultivars.