Research graph
References from Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Cancer statistics, 2025
2025 · External reference
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2024 · External reference
CancerPPD: a database of anticancer peptides and proteins
10.1093/nar/gku892 · 2015 · External reference
Deep learning improves antimicrobial peptide recognition
10.1093/bioinformatics/bty179 · 2018 · External reference
iACP: a sequence-based tool for identifying anticancer peptides
10.18632/oncotarget.7815 · 2016 · External reference
Amino acid encoding for deep learning applications
10.1186/s12859-020-03546-x · 2020 · External reference
DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
10.1016/j.omtn.2020.10.005 · 2020 · External reference
ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation
10.1016/j.omtn.2019.04.025 · 2019 · External reference
Graph-BERT and language model-based framework for protein–protein interaction identification
10.1038/s41598-023-31612-w · 2023 · External reference
Attention is All you Need
2017 · External reference
The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain
10.1073/pnas.37.4.205 · 1951 · External reference
Analysis of membrane and surface protein sequences with the hydrophobic moment plot
10.1016/0022-2836(84)90309-7 · 1984 · External reference
PepNN: a deep attention model for the identification of peptide binding sites
10.1038/s42003-022-03445-2 · 2022 · External reference
Efficient prediction of peptide self-assembly through sequential and graphical encoding
10.1093/bib/bbad409 · 2023 · External reference
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
10.1038/s41598-021-02703-3 · 2021 · External reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · 2021 · External reference
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
10.1093/bib/bbac630 · 2023 · External reference
Structure-aware deep model for MHC-II peptide binding affinity prediction
10.1186/s12864-023-09900-6 · 2024 · External reference
Convolutional Sequence to Sequence Learning
2017 · External reference
Self-Attention with Relative Position Representations
2018 · External reference
FNet: Mixing Tokens with Fourier Transforms
2022 · External reference
Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields
2023 · External reference
Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures
10.1063/5.0152833 · 2023 · External reference
Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides
10.1186/s12915-026-02520-y · 2026 · External reference
Mamba-ACP: a Hybrid State-Space and Transformer Framework for Interpretable Anticancer Peptide Prediction
10.1109/tcbbio.2026.3684898 · 2026 · External reference
pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning
10.1038/s41598-024-84146-0 · 2025 · External reference
BT-Trans ACP: A binary tree-growth transformer with hybrid deep learning for anticancer peptide prediction
2025 · External reference
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
10.1016/0005-2795(75)90109-9 · 1975 · External reference
Visualizing Data using t-SNE
2008 · External reference
Unresolved reference
1979 · External reference
Antimicrobial Peptides: Successes, Challenges and Unanswered Questions
10.1007/s00232-011-9343-0 · 2011 · External reference
Designing antimicrobial peptides: form follows function
10.1038/nrd3591 · 2011 · External reference
Protein secondary structure prediction by using deep learning method
10.1016/j.knosys.2016.11.015 · 2017 · External reference
Peptide Stability in Drug Development: A Comparison of Peptide Reactivity in Different Biological Media
10.1002/jps.2600810802 · 1992 · External reference
The Role of Thiols and Disulfides on Protein Stability
10.2174/138920309789630534 · 2009 · External reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · 2021 · External reference
Neural Architecture Search with Reinforcement Learning
2017 · External reference
CancerPPD2: an updated repository of anticancer peptides and proteins
10.1093/database/baaf030 · 2025 · External reference
Unresolved reference
External reference
APD3: the antimicrobial peptide database as a tool for research and education
10.1093/nar/gkv1278 · 2016 · External reference
CD-HIT: accelerated for clustering the next-generation sequencing data
10.1093/bioinformatics/bts565 · 2012 · External reference
Wavelets in bioinformatics and computational biology:state of art and perspectives
10.1093/bioinformatics/19.1.2 · 2003 · External reference
Unresolved reference
1999 · External reference
Deep Learning for Protein–Protein Interaction Site Prediction
2021 · External reference
Unresolved reference
1999 · External reference
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
2020 · External reference
A survey of the recent architectures of deep convolutional neural networks
10.1007/s10462-020-09825-6 · 2020 · External reference
Language Modeling with Gated Convolutional Networks
2017 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
Unresolved reference
2015 · External reference
Unresolved reference
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