ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Mar 1, 2023
Abstract
MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decades, machine learning-based methods have been developed, accelerating the discovery of SSPs to some extent. However, existing methods highly depend on handcrafted feature engineering, which easily ignores the latent feature representations and impacts the predictive performance.