Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

ABSTRACT:

Motivation

With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functional therapeutic peptides (MFTP) via sequence-based computational tools.

Results

Here we propose a novel multi-label-based method, named ETFC, to predict 21 categories of therapeutic peptides. The method utilizes a deep learning-based model architecture, which consists of four blocks: embedding, text convolutional neural network, feed-forward network, and classification blocks. This method also adopts an imbalanced learning strategy with a novel multi-label focal dice loss function (MLFDL). MLFDL is applied in the ETFC method to solv

SUBMITTER: Fan H 

PROVIDER: S-EPMC10234765 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature