AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.

ABSTRACT: Cancer remains a major global health issue, causing numerous deaths annually. Standard treatments like chemotherapy and radiotherapy often exhibit cytotoxic characteristics, harming healthy cells and leading to significant side effects. In this context, anticancer peptides (ACPs) offer a promising strategy by specifically triggering apoptosis in cancer cells while protecting healthy tissues. However, the experimental screening process for new ACPs involves significant costs and requires considerable labor. To address these challenges, we propose an advanced computational framework: AttBiLSTM_DE, which combines an Attention-based Bidirectional LSTM architecture with Optimized Weighted Features for accurate ACP predictions. Firstly, we employed four NLP-based feature encoding techniques: One

SUBMITTER: Juthy MJN 

PROVIDER: S-EPMC12770317 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature