ArticleScientific reports2022
ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dual Activity Microbial Peptides Catalog.Scientific data · 2026Article
- ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.Molecular diversity · 2026Article
- Multi-ACPNet: A multi-scale sequence-structure feature fusion framework for anticancer peptide identification and functional prediction.PLoS computational biology · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- MetaPepticon: automated prediction of anticancer peptides from microbial genomes and metagenomes.PeerJ · 2026Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
- HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis.PLoS computational biology · 2025Article
- HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors.Journal of pharmaceutical analysis · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.PloS one · 2024Article
- Examining the functional space of gut microbiome-derived peptides.MicrobiologyOpen · 2023Article
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5 authors.
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Abstract
Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .
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