Efficient prediction of anticancer peptides through deep learning.

ABSTRACT:

Background

Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides.

Objective

This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods.

Methods

A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public database

SUBMITTER: Salam A 

PROVIDER: S-EPMC11323142 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature