ArticleBMC bioinformatics2024
DPI_CDF: druggable protein identifier using cascade deep forest.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- An Explainable Deep Learning Model for Clathrin Protein Prediction Using a DCT-Enhanced Position-Specific Scoring Matrix.Current drug targets · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- DeepBBMC biology · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.Protein science : a publication of the Protein Society · 2025Article
- DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods.Briefings in bioinformatics · 2025Article
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy.Scientific reports · 2025Article
- Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease.Biomedical engineering and computational biology · 2025Article
- TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.Briefings in bioinformatics · 2024Article
- Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.BMC bioinformatics · 2024Article
- StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.IET systems biologyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.
methodsIn this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.
resultsThe empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY: The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.