ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

Abstract

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

“…AMPfun ( Chung et al, 2020 ) and xDeep-AcPEP ( Chen J. et al, 2021 ) have Using deep learning methods to predict the multifaceted functionalities of peptides, while Alsanea et al employed ensemble techniques for ACPs prediction ( Alsanea et al, 2022 ). Advanced models like ME-ACP ( Feng et al, 2021 ) and ACP-DA ( Chen X. G. et al, 2021 ) which successfully integrated neural network architectures and data balancing techniques. Equally impressive is the approach taken by Lv et al that married the light gradient booster with deep representation learning algorithms ( Lv et al, 2021a ).…”