Abstract
Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.
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References
Boopathi, V., Subramaniyam, S., Malik, A., Lee, G., Manavalan, B., Yang, D.C.: Macppred: a support vector machine-based meta-predictor for identification of anticancer peptides. Int. J. Molecular Sci. 20(8), 1964 (2019). https://doi.org/10.3390/ijms20081964. https://pubmed.ncbi.nlm.nih.gov/31013619, 31013619[pmid]
Chen, W., Ding, H., Feng, P., Lin, H., Chou, K.C.: iacp: a sequence-based tool for identifying anticancer peptides. Oncotarget 7(13), 16895–16909 (2016). https://www.ncbi.nlm.nih.gov/pubmed/26942877[pmid]
Coates, A., Abraham, S., Kaye, S., Sowerbutts, T., Frewin, C., Fox, R.,Tattersall, M.: On the receiving end — patient perception of the side-effects of cancer chemotherapy. Euro. J. Cancer Clinical Oncol. 19(2), 203–208 (1983).https://doi.org/10.1016/0277-5379(83)90418-2,http://www.sciencedirect.com/science/article/pii/0277537983904182
Gaspar, D., Veiga, A.S., Castanho, M.A.: From antimicrobial to anticancer peptides a review. Front. Microbiol. 4, 294 (2013). https://doi.org/10.3389/fmicb.2013.00294
Grisoni, F., Neuhaus, C.S., Gabernet, G., Muller, A.T., Hiss, J.A, Schneider, G.: Designing anticancer peptides by constructive machine learning. ChemMedChem, 13(13), 1300–1302 (2018). https://doi.org/10.1002/cmdc.201800204, https://onlinelibrary.wiley.com/doi/abs/10.1002/cmdc.201800204
Grisoni, F., Neuhaus, C.S., Hishinuma, M., Gabernet, G., Hiss, J.A., Kotera, M., Schneider, G.: De novo design of anticancer peptides by ensemble artificial neural networks. J. Molecular Model. 25(5), 112 (2019). https://doi.org/10.1007/s00894-019-4007-6
Harris, F., Dennison, S.R., Singh, J., Phoenix, D.A.: On the selectivity and efficacy of defense peptides with respect to cancer cells. Med. Res. Rev. 33(1), 190–234 (2013). https://doi.org/10.1002/med.20252
Hoskin, D.W., Ramamoorthy, A.: Studies on anticancer activities of antimicrobial peptides. Biochimica et Biophysica Acta (BBA) - Biomembranes 1778(2), 357 – 375 (2008). https://doi.org/10.1016/j.bbamem.2007.11.008
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015)
Longley, D., Johnston, P.: Molecular mechanisms of drug resistance. J. Pathol. 205(2), 275–292 (2005). https://doi.org/10.1002/path.1706. https://onlinelibrary.wiley.com/doi/abs/10.1002/path.1706
Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)
Mahmud, M., Kaiser, M.S., Hussain, A.: Deep learning in mining biological data (2020)
Manavalan, B., Basith, S., Shin, T.H., Choi, S., Kim, M.O., Lee, G.: Mlacp: machine-learning-based prediction of anticancer peptides. Oncotarget 8(44), 77121–77136 (2017)
Meher, P.K., Sahu, T.K., Saini, V., Rao, A.R.: Predicting antimicrobialpeptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into chou’s general pseaac. Scientific Reports 7(42362) (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc. (2013). http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., Raghava, G.P.S.: In silico models for designing and discovering novel anticancer peptides. Sci. Rep. 3(2984), 2045–2322 (2013)
Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., Raghava, G.P.S.: In silico models for designing and discovering novel anticancer peptides. Sci. Rep. 3(1), 2984 (2013). https://doi.org/10.1038/srep02984
Wu, C., Gao, R., Zhang, Y., De Marinis, Y.: PTPD: predicting therapeutic peptides by deep learning and word2vec. BMC Bioinform. 20(1), 456 (2019). https://doi.org/10.1186/s12859-019-3006-z
Yi, H.C., You, Z.H., Zhou, X., Cheng, L., Li, X., Jiang, T.H., Chen, Z.H.: ACP-DL: a deep learning long short-term memory model to predict anticancerpeptides using high-efficiency feature representation. Molecular therapy. Nucleic acids 17, 1–9 (2019).https://doi.org/10.1016/j.omtn.2019.04.025,https://www.ncbi.nlm.nih.gov/pubmed/31173946, 31173946[pmid]
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Lane, N., Kahanda, I. (2021). DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_7
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