NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides
Issued Date
2022-09-01
Resource Type
ISSN
00104825
eISSN
18790534
Scopus ID
2-s2.0-85132841955
Pubmed ID
35715261
Journal Title
Computers in Biology and Medicine
Volume
148
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers in Biology and Medicine Vol.148 (2022)
Suggested Citation
Charoenkwan P., Schaduangrat N., Lio' P., Moni M.A., Manavalan B., Shoombuatong W. NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. Computers in Biology and Medicine Vol.148 (2022). doi:10.1016/j.compbiomed.2022.105700 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84263
Title
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides
Other Contributor(s)
Abstract
Tumor homing peptides (THPs) play a crucial role in recognizing and specifically binding to cancer cells. Although experimental approaches can facilitate the precise identification of THPs, they are usually time-consuming, labor-intensive, and not cost-effective. However, computational approaches can identify THPs by utilizing sequence information alone, thus highlighting their great potential for large-scale identification of THPs. Herein, we propose NEPTUNE, a novel computational approach for the accurate and large-scale identification of THPs from sequence information. Specifically, we constructed variant baseline models from multiple feature encoding schemes coupled with six popular machine learning algorithms. Subsequently, we comprehensively assessed and investigated the effects of these baseline models on THP prediction. Finally, the probabilistic information generated by the optimal baseline models is fed into a support vector machine-based classifier to construct the final meta-predictor (NEPTUNE). Cross-validation and independent tests demonstrated that NEPTUNE achieved superior performance for THP prediction compared with its constituent baseline models and the existing methods. Moreover, we employed the powerful SHapley additive exPlanations method to improve the interpretation of NEPTUNE and elucidate the most important features for identifying THPs. Finally, we implemented an online web server using NEPTUNE, which is available at http://pmlabstack.pythonanywhere.com/NEPTUNE. NEPTUNE could be beneficial for the large-scale identification of unknown THP candidates for follow-up experimental validation.