DPI_CDF: druggable protein identifier using cascade deep forest
Issued Date
2024-12-01
Resource Type
eISSN
14712105
Scopus ID
2-s2.0-85189624805
Journal Title
BMC Bioinformatics
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Bioinformatics Vol.25 No.1 (2024)
Suggested Citation
Arif M., Fang G., Ghulam A., Musleh S., Alam T. DPI_CDF: druggable protein identifier using cascade deep forest. BMC Bioinformatics Vol.25 No.1 (2024). doi:10.1186/s12859-024-05744-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/97955
Title
DPI_CDF: druggable protein identifier using cascade deep forest
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Drug 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. Methods: In 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. Results: The 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.