G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer

References

• Agrawal, P., Bhagat, D., Mahalwal, M., Sharma, N., & Raghava, G. P. (2021). AntiCP 2.0: An updated model for predicting anticancer peptides. Briefings in Bioinformatics, 22(3), bbaa153. https://doi.org/10.1093/bib/bbaa153
   PubMed Web of Science ®Google Scholar
• Alsanea, M., Dukyil, A., Riaz, B., Alebeisat, F., Islam, M., Habib, S., & Afnan  . (2022) To assist oncologists: An efficient machine learning-based approach for anti-cancer peptides classification. Sensors, vol. 22(11), 4005. https://doi.org/10.3390/s22114005
   PubMed Web of Science ®Google Scholar
• Alsina, M., Arrazubi, V., Diez, M., & Tabernero, J. (2023). Current developments in gastric cancer: From molecular profiling to treatment strategy. Nature Reviews. Gastroenterology & Hepatology, 20(3), 155–170. https://doi.org/10.1038/s41575-022-00703-w
   PubMed Web of Science ®Google Scholar
• Basith, S., Cui, M., Macalino, S. J., & Choi, S. (2017). Expediting the design, discovery and development of anticancer drugs using computational approaches. Current Medicinal Chemistry, 24(42), 4753–4778. https://doi.org/10.2174/0929867323666160902160535
   PubMed Web of Science ®Google Scholar
• Boohaker, R. J., Lee, M. W., Vishnubhotla, P., Perez, J. L. M., & Khaled, A. R. (2012). The use of therapeutic peptides to target and to kill cancer cells. Current Medicinal Chemistry, 19(22), 3794–3804. https://doi.org/10.2174/092986712801661004
   PubMed Web of Science ®Google Scholar
• Boopathi, V., Subramaniyam, S., Malik, A., Lee, G., Manavalan, B., & Yang, D.-C. (2019). mACPpred: A support vector machine-based meta-predictor for identification of anticancer peptides. International Journal of Molecular Sciences, 20(8), 1964. https://doi.org/10.3390/ijms20081964
   PubMed Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
   Web of Science ®Google Scholar
• Cao, R., Adhikari, B., Bhattacharya, D., Sun, M., Hou, J., & Cheng, J. (2017). QAcon: Single model quality assessment using protein structural and contact information with machine learning techniques. Bioinformatics (Oxford, England), 33(4), 586–588. https://doi.org/10.1093/bioinformatics/btw694
   PubMed Web of Science ®Google Scholar
• CD-HIT: Cluster Database at High Identity with Tolerance. (2023). [Online]. https://www.bioinformatics.org/cd-hit/
   Google Scholar
• Chen, J., Cheong, H. H., & Siu, S. W. I. (2021). xDeep-AcPEP: Deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. Journal of Chemical Information and Modeling, 61(8), 3789–3803. https://doi.org/10.1021/acs.jcim.1c00181
   PubMed Web of Science ®Google Scholar
• Chen, W., Ding, H., Feng, P., Lin, H., & Chou, K.-C. (2016). iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget, 7(13), 16895–16909. https://doi.org/10.18632/oncotarget.7815
   PubMedGoogle Scholar
• Chen, X., Liu, S., & Zhang, W. (2022). Predicting coding potential of RNA sequences by solving local data imbalance. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(2), 1075–1083. https://doi.org/10.1109/TCBB.2020.3021800
   PubMed Web of Science ®Google Scholar
• Chen, X., Yang, X., Li, C., Lin, X., & Zhang, W. (2023). Non-coding RNA identification with pseudo RNA sequences and feature representation learning. Computers in Biology and Medicine, 165, 107355. https://doi.org/10.1016/j.compbiomed.2023.107355
   PubMed Web of Science ®Google Scholar
• Chen, X., Zhang, W., Yang, X., Li, C., & Chen, H. (2021). ACP-DA: Improving the prediction of anticancer peptides using data augmentation. Frontiers in Genetics, 12, 698477. https://doi.org/10.3389/fgene.2021.698477
   PubMed Web of Science ®Google Scholar
• Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Wang, Y., Webb, G. I., Smith, A. I., Daly, R. J., Chou, K.-C., & Song, J. (2018). iFeature: A python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics (Oxford, England), 34(14), 2499–2502. https://doi.org/10.1093/bioinformatics/bty140
   PubMed Web of Science ®Google Scholar
• Chou, K.-C. (2009). Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics, 6(4), 262–274. https://doi.org/10.2174/157016409789973707
   Web of Science ®Google Scholar
• Chou, K.-C. (2011). Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology, 273(1), 236–247. https://doi.org/10.1016/j.jtbi.2010.12.024
   PubMed Web of Science ®Google Scholar
• Churpek, M. M., Carey, K. A., Edelson, D. P., Singh, T., Astor, B. C., Gilbert, E. R., Winslow, C., Shah, N., Afshar, M., & Koyner, J. L. (2020). Internal and external validation of a machine learning risk score for acute kidney injury. JAMA Network Open, 3(8), e2012892–e2012892. https://doi.org/10.1001/jamanetworkopen.2020.12892
   Web of Science ®Google Scholar
• Deplanque, G., Madhusudan, S., Jones, P. H., Wellmann, S., Christodoulos, K., Talbot, D. C., Ganesan, T. S., Blann, A., & Harris, A. L. (2004). Phase II trial of the antiangiogenic agent IM862 in metastatic renal cell carcinoma. British Journal of Cancer, 91(9), 1645–1650. https://doi.org/10.1038/sj.bjc.6602126
   PubMed Web of Science ®Google Scholar
• Feng, P.-M., Chen, W., Lin, H., & Chou, K.-C. (2013). iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Analytical Biochemistry, 442(1), 118–125. https://doi.org/10.1016/j.ab.2013.05.024
   PubMed Web of Science ®Google Scholar
• Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer, 149(4), 778–789. https://doi.org/10.1002/ijc.33588
   Web of Science ®Google Scholar
• Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research, 61, 863–905. https://doi.org/10.1613/jair.1.11192
   Web of Science ®Google Scholar
• Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Garai, S., Thomas, J., Dey, P., & Das, D. (2023). LGBM-ACp: An ensemble model for anticancer peptide prediction and in silico screening with potential drug targets. Molecular Diversity, 1–17. https://doi.org/10.1007/s11030-023-10602-0
   Web of Science ®Google Scholar
• Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
   Web of Science ®Google Scholar
• Hajisharifi, Z., Piryaiee, M., Beigi, M. M., Behbahani, M., & Mohabatkar, H. (2014). Predicting anticancer peptides with Chou′ s pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology, 341, 34–40. https://doi.org/10.1016/j.jtbi.2013.08.037
   PubMed Web of Science ®Google Scholar
• Hwang, J. S., Kim, S. G., Shin, T. H., Jang, Y. E., Kwon, D. H., & Lee, G. (2022). Development of anticancer peptides using artificial intelligence and combinational therapy for cancer therapeutics. Pharmaceutics, 14(5), 997. https://doi.org/10.3390/pharmaceutics14050997
   PubMed Web of Science ®Google Scholar
• Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: A Cancer Journal for Clinicians, 61(2), 69–90. https://doi.org/10.3322/caac.20107
   PubMed Web of Science ®Google Scholar
• Kabir, M., Arif, M., Ahmad, S., Ali, Z., Swati, Z. N. K., & Yu, D.-J. (2018). Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information. Chemometrics and Intelligent Laboratory Systems, 182, 158–165. https://doi.org/10.1016/j.chemolab.2018.09.007
   Web of Science ®Google Scholar
• Kingsford, C., & Salzberg, S. L. (2008). What are decision trees? Nature Biotechnology, 26(9), 1011–1013. https://doi.org/10.1038/nbt0908-1011
   PubMed Web of Science ®Google Scholar
• Larose, D. T., & Larose, C. D. (2014). k-nearest neighbor algorithm.
   Google Scholar
• LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395–2399. https://doi.org/10.1161/CIRCULATIONAHA.106.682658
   PubMed Web of Science ®Google Scholar
• Leung, K. M. (2007). Naive Bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2007, 123–156.
   Google Scholar
• Leuschner, C., & Hansel, W. (2004). Membrane disrupting lytic peptides for cancer treatments. Current Pharmaceutical Design, 10(19), 2299–2310. https://doi.org/10.2174/1381612043383971
   PubMed Web of Science ®Google Scholar
• Lin, H., & Li, Q. (2007). Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components. Journal of Computational Chemistry, 28(9), 1463–1466. https://doi.org/10.1002/jcc.20554
   PubMed Web of Science ®Google Scholar
• Lin, M.-W., Tseng, Y.-W., Shen, C.-C., Hsu, M.-N., Hwu, J.-R., Chang, C.-W., Yeh, C.-J., Chou, M.-Y., Wu, J.-C., & Hu, Y.-C. (2018). Synthetic switch-based baculovirus for transgene expression control and selective killing of hepatocellular carcinoma cells. Nucleic Acids Research, 46(15), e93–e93–e93. https://doi.org/10.1093/nar/gky447
   Web of Science ®Google Scholar
• Lv, Z., Cui, F., Zou, Q., Zhang, L., & Xu, L. (2021). Anticancer peptides prediction with deep representation learning features. Briefings in Bioinformatics, 22(5), bbab008. https://doi.org/10.1093/bib/bbab008
   PubMed Web of Science ®Google Scholar
• Manavalan, B., Basith, S., Shin, T. H., Choi, S., Kim, M. O., & Lee, G. (2017). MLACP: Machine-learning-based prediction of anticancer peptides. Oncotarget, 8(44), 77121–77136. https://doi.org/10.18632/oncotarget.20365
   PubMedGoogle Scholar
• Nasiri, F., Atanaki, F. F., Behrouzi, S., Kavousi, K., & Bagheri, M. (2021). CpACpP: In silico cell-penetrating anticancer peptide prediction using a novel bioinformatics framework. ACS Omega. 6(30), 19846–19859. https://doi.org/10.1021/acsomega.1c02569
   PubMed Web of Science ®Google Scholar
• Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565
   PubMed Web of Science ®Google Scholar
• Peng, Y., & Nagata, M. H. (2020). An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons, and Fractals, 139, 110055. https://doi.org/10.1016/j.chaos.2020.110055
   PubMed Web of Science ®Google Scholar
• Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883. https://doi.org/10.4249/scholarpedia.1883
   Google Scholar
• Pinacho-Castellanos, S. A., García-Jacas, C. R., Gilson, M. K., & Brizuela, C. A. (2021). Alignment-free antimicrobial peptide predictors: Improving performance by a thorough analysis of the largest available data set. Journal of Chemical Information and Modeling, 61(6), 3141–3157. https://doi.org/10.1021/acs.jcim.1c00251
   PubMed Web of Science ®Google Scholar
• Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine (New York, N.Y.), 47(1), 31–39. https://doi.org/10.17849/insm-47-01-31-39.1
   PubMedGoogle Scholar
• Schaduangrat, N., Nantasenamat, C., Prachayasittikul, V., & Shoombuatong, W. (2019). ACPred: A computational tool for the prediction and analysis of anticancer peptides. Molecules (Basel, Switzerland), 24(10), 1973. https://doi.org/10.3390/molecules24101973
   PubMed Web of Science ®Google Scholar
• Schapire, R. E. (2013). Explaining AdaBoost. In B. Schölkopf, Z. Luo, and V. Vovk (Eds.), Empirical inference (pp. 37–52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
   Google Scholar
• Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660
   PubMed Web of Science ®Google Scholar
• Suthaharan, S. (2016). Support vector machine. In Machine learning models and algorithms for big data classification, vol. 36, in Integrated Series in Information Systems, vol. 36 (pp. 207–235). Springer US. https://doi.org/10.1007/978-1-4899-7641-3_9
   Google Scholar
• Thundimadathil, J. (2012). Cancer treatment using peptides: Current therapies and future prospects. Journal of Amino Acids, 2012, 967347–967313. https://doi.org/10.1155/2012/967347
   PubMedGoogle Scholar
• Tornesello, A. L., Borrelli, A., Buonaguro, L., Buonaguro, F. M., & Tornesello, M. L. (2020). Antimicrobial peptides as anticancer agents: Functional properties and biological activities. Molecules (Basel, Switzerland), 25(12), 2850. https://doi.org/10.3390/molecules25122850
   PubMed Web of Science ®Google Scholar
• Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., & Raghava, G. P. S. (2013). In silico models for designing and discovering novel anticancer peptides. Scientific Reports, 3(1), 2984. https://doi.org/10.1038/srep02984
   PubMedGoogle Scholar
• Tyagi, A., Tuknait, A., Anand, P., Gupta, S., Sharma, M., Mathur, D., Joshi, A., Singh, S., Gautam, A., & Raghava, G. P. S. (2015). CancerPPD: A database of anticancer peptides and proteins. Nucleic Acids Research, 43, D837–D843. https://doi.org/10.1093/nar/gku892
   PubMed Web of Science ®Google Scholar
• Vlieghe, P., Lisowski, V., Martinez, J., & Khrestchatisky, M. (2010). Synthetic therapeutic peptides: Science and market. Drug Discovery Today. 15(1–2), 40–56. https://doi.org/10.1016/j.drudis.2009.10.009
   PubMed Web of Science ®Google Scholar
• Wang, H., Zhao, J., Zhao, H., Li, H., & Wang, J. (2021). CL-ACP: A parallel combination of CNN and LSTM anticancer peptide recognition model. BMC Bioinformatics, 22(1), 512. Dec. https://doi.org/10.1186/s12859-021-04433-9
   PubMed Web of Science ®Google Scholar
• Webb, G. I., Keogh, E., & Miikkulainen, R. (2010). Naïve Bayes. Encyclopedia of Machine Learning, 15(1), 713–714.
   Google Scholar
• Wei, L., Zhou, C., Chen, H., Song, J., & Su, R. (2018). ACPred-FL: A sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (Oxford, England), 34(23), 4007–4016. https://doi.org/10.1093/bioinformatics/bty451
   PubMed Web of Science ®Google Scholar
• Wu, C., Gao, R., Zhang, Y., & De Marinis, Y. (2019). PTPD: Predicting therapeutic peptides by deep learning and word2vec. BMC Bioinformatics, 20(1), 456. https://doi.org/10.1186/s12859-019-3006-z
   PubMed Web of Science ®Google Scholar
• Wu, X., Zeng, W., Lin, F., Xu, P., & Li, X. (2022). Anticancer peptide prediction via multi-kernel CNN and attention model. Frontiers in Genetics, 13, 887894.
   PubMed Web of Science ®Google Scholar
• Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2), 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
   Google Scholar