ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

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.

Results

Here, we propose ExamPle, a novel deep learning model using Siamese network and multi-view representation for the explainable prediction of the plant SSPs. Benchmarking comparison results show that our ExamPle performs significantly better than exist

SUBMITTER: Li Z 

PROVIDER: S-EPMC10027287 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature