Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation

Abstract

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

“…Previous studies have demonstrated that incorporating such evolutionary information can enhance the performance of protein function prediction tasks (An et al, 2019; Li & Liu, 2020). Finally, the Zsl matrix of amino acids provides rich information on physicochemical properties, which is crucial for building robust models in multi‐omics tasks (Lin et al, 2021; Yao et al, 2023).…”

“…The binary profile is a widely used technique for representing amino acid sequences (Qureshi et al, 2015; Yao et al, 2023). This method employs a one‐hot encoding strategy to capture both the composition and order information of a given sequence.…”

“…The BLOSUM62 matrix is a commonly used substitution matrix in bioinformatics to assess the similarity of protein sequences, based on observed frequencies of amino acid substitutions in a database of aligned sequences with less than 62% sequence identity (Trivedi & Nagarajaram, 2020). The BLOSUM62 matrix is a widely employed method for indicating protein evolutionary information and has been utilized in numerous bioinformatics tasks (Chen et al, 2021; Ma et al, 2022; Wei et al, 2022; Yao et al, 2023). In this study, we have employed the BLOSUM62 matrix to encode both AFPs and non‐AFPs.…”

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation

“…Compared to conventional machine learning methods such as random forests, this cascade architecture ensures that the deep forest algorithm can achieve profound feature exploration, thereby guaranteeing model performance. Within recent years, deep forests have also been employed on bioinformatics missions such as biological sequence analysis and multiomics data analysis. , …”

“…Compared to conventional machine learning methods such as random forests, this cascade architecture ensures that the deep forest algorithm can achieve profound feature exploration, thereby guaranteeing model performance. Within recent years, deep forests have also been employed on bioinformatics missions such as biological sequence analysis 14 and multiomics data analysis. 15,16 In this study, we proposed a novel framework for ATP identification, called ATPfinder, which enables efficient and accurate identification of ATP from collections of natural or synthetic sequences without extensive labor.…”