Research graph
References from HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis. Local targets link to admitted publications; unresolved targets remain external evidence.
Cancer statistics, 2024
2024 · External reference
Cancer statistics, 2023
2023 · External reference
Geometric deep learning for drug discovery
10.1016/j.eswa.2023.122498 · 2024 · External reference
Current research status of anti-cancer peptides: Mechanism of action, production, and clinical applications
10.1016/j.biopha.2023.114996 · 2023 · External reference
Peptides as multifunctional players in cancer therapy
10.1038/s12276-023-01016-x · 2023 · External reference
Bridging machine learning and peptide design for cancer treatment: a comprehensive review
10.1007/s10462-025-11148-3 · 2025 · External reference
Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set
10.1109/tcbb.2013.146 · 2014 · External reference
Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives
10.2174/0113894501322734241008163304 · 2024 · External reference
BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo
2023 · External reference
Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction
2023 · External reference
DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis
10.1093/nar/gkad055 · 2023 · External reference
Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering
10.1109/jbhi.2024.3357979 · 2024 · External reference
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
10.3390/molecules24101973 · 2019 · External reference
ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction
10.1038/s41598-022-24404-1 · 2022 · External reference
mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations
10.1016/j.jmb.2024.168687 · 2024 · External reference
ACP-ML: A sequence-based method for anticancer peptide prediction
10.1016/j.compbiomed.2024.108063 · 2024 · External reference
Unresolved reference
2024 · External reference
Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
10.1038/s41598-021-93124-9 · 2021 · External reference
Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings
10.3389/fmed.2023.1291352 · 2024 · External reference
A First Computational Frame for Recognizing Heparin-Binding Protein
10.3390/diagnostics13142465 · 2023 · External reference
Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information
10.1016/j.ins.2016.06.026 · 2017 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2021 · External reference
SeqKit2: A Swiss army knife for sequence and alignment processing
10.1002/imt2.191 · 2024 · External reference
Unresolved reference
External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
Random forests
10.1007/978-3-030-56485-8_3 · 2020 · External reference
A comparative analysis of gradient boosting algorithms
10.1007/s10462-020-09896-5 · 2021 · External reference
Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees
10.1007/978-3-030-37334-4_4 · 2019 · External reference
Accurately identifying hemagglutinin using sequence information and machine learning methods
10.3389/fmed.2023.1281880 · 2023 · External reference
Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching
2024 · External reference
Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data
10.1186/s12915-024-02085-8 · 2024 · External reference
Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation
10.1186/s12915-024-01968-0 · 2024 · External reference
RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing
2025 · External reference
TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning
2024 · External reference
BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models
2021 · External reference
Challenges in KNN classification
2021 · External reference
Logistic regression
10.1038/nmeth.3904 · 2016 · External reference
Decision tree methods: applications for classification and prediction
2015 · External reference
Exploring the anticancer potential of cricket-derived peptides in human cancer cells; pro-apoptotic effects via a caspase-3 pathway
10.1016/j.jff.2025.106760 · 2025 · External reference
Antimicrobial peptides: mechanism of action, activity and clinical potential
2021 · External reference
Interpretable machine learning with an ensemble of gradient boosting machines
10.1016/j.knosys.2021.106993 · 2021 · External reference
Machine learning approaches in microbiome research: challenges and best practices
10.3389/fmicb.2023.1261889 · 2023 · External reference
Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines
10.1038/s41392-024-02107-5 · 2025 · External reference
