NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides
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 ca...
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th-mahidol.737582022-08-04T11:34:55Z NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides Phasit Charoenkwan Nalini Schaduangrat Pietro Lio' Mohammad Ali Moni Balachandran Manavalan Watshara Shoombuatong Department of Computer Science and Technology The University of Queensland Mahidol University Sungkyunkwan University Chiang Mai University Computer Science Medicine 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. 2022-08-04T03:54:08Z 2022-08-04T03:54:08Z 2022-01-01 Article Computers in Biology and Medicine. (2022) 10.1016/j.compbiomed.2022.105700 18790534 00104825 2-s2.0-85132841955 https://repository.li.mahidol.ac.th/handle/123456789/73758 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132841955&origin=inward |
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Computer Science Medicine Phasit Charoenkwan Nalini Schaduangrat Pietro Lio' Mohammad Ali Moni Balachandran Manavalan Watshara Shoombuatong NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
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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. |
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Department of Computer Science and Technology |
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Department of Computer Science and Technology Phasit Charoenkwan Nalini Schaduangrat Pietro Lio' Mohammad Ali Moni Balachandran Manavalan Watshara Shoombuatong |
format |
Article |
author |
Phasit Charoenkwan Nalini Schaduangrat Pietro Lio' Mohammad Ali Moni Balachandran Manavalan Watshara Shoombuatong |
author_sort |
Phasit Charoenkwan |
title |
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
title_short |
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
title_full |
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
title_fullStr |
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
title_full_unstemmed |
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides |
title_sort |
neptune: a novel computational approach for accurate and large-scale identification of tumor homing peptides |
publishDate |
2022 |
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https://repository.li.mahidol.ac.th/handle/123456789/73758 |
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1763494890036002816 |