Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides

Shaherin       Basith; Balachandran       Manavalan; Tae    Hwan    Shin; Da Yeon      Lee; Gwang       Lee
doi:10.2174/1389203721666200117171403
Abstract

Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs.
Keywords: Cancer, anticancer peptides, machine learning, support vector machine, random forest, ACPs.
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[1] 
Arnold, M.; Karim-Kos, H.E.; Coebergh, J.W.; Byrnes, G.; Antilla, A.; Ferlay, J.; Renehan, A.G.; Forman, D.; Soerjomataram, I. Recent trends in incidence of five common cancers in 26 European countries since 1988: Analysis of the European Cancer Observatory. Eur. J. Cancer,  2015, 51(9), 1164-1187.
[http://dx.doi.org/10.1016/j.ejca.2013.09.002] [PMID:  24120180] 
[2] 
Thundimadathil, J. Cancer treatment using peptides: current therapies and future prospects. J. Amino Acids, 2012.2012967347
[http://dx.doi.org/10.1155/2012/967347] [PMID:  23316341] 
[3] 
Basith, S.; Cui, M.; Macalino, S.J.Y.; Choi, S. Expediting the Design, Discovery and Development of Anticancer Drugs using Computational Approaches. Curr. Med. Chem.,  2017, 24(42), 4753-4778.
[PMID: 27593958] 
[4] 
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. Mol. Sci.,  2019, 20(8)E1964
[http://dx.doi.org/10.3390/ijms20081964] [PMID:  31013619] 
[5] 
Manavalan, B.; Basith, S.; Shin, T.H.; Choi, S.; Kim, M.O.; Lee, G. MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget,  2017, 8(44), 77121-77136.
[http://dx.doi.org/10.18632/oncotarget.20365] [PMID:  29100375] 
[6] 
Dhanda, S.K.; Usmani, S.S.; Agrawal, P.; Nagpal, G.; Gautam, A.; Raghava, G.P.S. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief. Bioinform.,  2017, 18(3), 467-478.
[PMID: 27016393] 
[7] 
Usmani, S.S.; Bedi, G.; Samuel, J.S.; Singh, S.; Kalra, S.; Kumar, P.; Ahuja, A.A.; Sharma, M.; Gautam, A.; Raghava, G.P.S. THPdb: Database of FDA-approved peptide and protein therapeutics. PLoS One,  2017, 12(7)e0181748
[http://dx.doi.org/10.1371/journal.pone.0181748] [PMID:  28759605] 
[8] 
Usmani, S.S.; Bhalla, S.; Raghava, G.P.S. Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features. Front. Pharmacol.,  2018, 9, 954.
[http://dx.doi.org/10.3389/fphar.2018.00954] [PMID:  30210341] 
[9] 
Usmani, S.S.; Kumar, R.; Bhalla, S.; Kumar, V.; Raghava, G.P.S. In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. Adv. Protein Chem. Struct. Biol.,  2018, 112, 221-263.
[http://dx.doi.org/10.1016/bs.apcsb.2018.01.006] [PMID:  29680238] 
[10] 
Usmani, S.S.; Kumar, R.; Kumar, V.; Singh, S.; Raghava, G.P.S. AntiTbPdb: a knowledgebase of anti-tubercular peptides.,  Database (Oxford), 2018. http://dx.doi.org/10.1093/database/bay025
[11] 
Usmani, S.S.; Agrawal, P.; Sehgal, M.; Patel, P.K.; Raghava, G.P.S. ImmunoSPdb: an archive of immunosuppressive peptides.,  Database (Oxford), 2019. http://dx.doi.org/10.1093/database/baz012
[12] 
Vlieghe, P.; Lisowski, V.; Martinez, J.; Khrestchatisky, M. Synthetic therapeutic peptides: science and market. Drug Discov. Today,  2010, 15(1-2), 40-56.
[http://dx.doi.org/10.1016/j.drudis.2009.10.009] [PMID:  19879957] 
[13] 
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.,  2013, 33(1), 190-234.
[http://dx.doi.org/10.1002/med.20252] [PMID:  21922503] 
[14] 
Boohaker, R.J.; Lee, M.W.; Vishnubhotla, P.; Perez, J.M.; Khaled, A.R. The use of therapeutic peptides to target and to kill cancer cells. Curr. Med. Chem.,  2012, 19(22), 3794-3804.
[http://dx.doi.org/10.2174/092986712801661004] [PMID:  22725698] 
[15] 
Boohaker, R.J.; Zhang, G.; Lee, M.W.; Nemec, K.N.; Santra, S.; Perez, J.M.; Khaled, A.R. Rational development of a cytotoxic peptide to trigger cell death. Mol. Pharm.,  2012, 9(7), 2080-2093.
[http://dx.doi.org/10.1021/mp300167e] [PMID:  22591113] 
[16] 
Tyagi, A.; Tuknait, A.; Anand, P.; Gupta, S.; Sharma, M.; Mathur, D.; Joshi, A.; Singh, S.; Gautam, A.; Raghava, G.P.; Cancer, P.P.D. CancerPPD: a database of anticancer peptides and proteins. Nucleic Acids Res.,  2015, 43(Database issue), D837-D843.
[http://dx.doi.org/10.1093/nar/gku892] [PMID:  25270878] 
[17] 
Gaspar, D.; Veiga, A.S.; Castanho, M.A. From antimicrobial to anticancer peptides. A review. Front. Microbiol.,  2013, 4, 294.
[http://dx.doi.org/10.3389/fmicb.2013.00294] [PMID:  24101917] 
[18] 
Felício, M.R.; Silva, O.N.; Gonçalves, S.; Santos, N.C.; Franco, O.L. Peptides with Dual Antimicrobial and Anticancer Activities. Front Chem.,  2017, 5, 5.
[http://dx.doi.org/10.3389/fchem.2017.00005] [PMID:  28271058] 
[19] 
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget,  2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID:  26942877] 
[20] 
Khan, F.; Akbar, S.; Basit, A.; Khan, I.; Akhlaq, H. Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering,  2017, pp. 91-96.
[21] 
Li, F.M.; Wang, X.Q. Identifying anticancer peptides by using improved hybrid compositions. Sci. Rep.,  2016, 6, 33910.
[http://dx.doi.org/10.1038/srep33910] [PMID:  27670968] 
[22] 
Tyagi, A.; Kapoor, P.; Kumar, R.; Chaudhary, K.; Gautam, A.; Raghava, G.P. In silico models for designing and discovering novel anticancer peptides. Sci. Rep.,  2013, 3, 2984.
[http://dx.doi.org/10.1038/srep02984] [PMID:  24136089] 
[23] 
Vijayakumar, S.; Lakshmi, P. ACPP: a web server for prediction and design of anti-cancer peptides. Int. J. Pept. Res. Ther.,  2015, 21(1), 99-106.
[http://dx.doi.org/10.1007/s10989-014-9435-7] 
[24] 
Wei, L.; Zhou, C.; Chen, H.; Song, J.; Su, R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics,  2018, 34(23), 4007-4016.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID:  29868903] 
[25] 
Xu, L.; Liang, G.; Wang, L.; Liao, C. A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides. Genes (Basel),  2018, 9(3)E158
[http://dx.doi.org/10.3390/genes9030158] [PMID:  29534013] 
[26] 
Giguère, S.; Laviolette, F.; Marchand, M.; Tremblay, D.; Moineau, S.; Liang, X.; Biron, É.; Corbeil, J. Machine learning assisted design of highly active peptides for drug discovery. PLOS Comput. Biol.,  2015, 11(4)e1004074
[http://dx.doi.org/10.1371/journal.pcbi.1004074] [PMID:  25849257] 
[27] 
Wang, G.; Li, X.; Wang, Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res.,  2016, 44(D1), D1087-D1093.
[http://dx.doi.org/10.1093/nar/gkv1278] [PMID:  26602694] 
[28] 
Novković, M.; Simunić, J.; Bojović, V.; Tossi, A.; Juretić, D. DADP: the database of anuran defense peptides. Bioinformatics,  2012, 28(10), 1406-1407.
[http://dx.doi.org/10.1093/bioinformatics/bts141] [PMID:  22467909] 
[29] 
Gogoladze, G.; Grigolava, M.; Vishnepolsky, B.; Chubinidze, M.; Duroux, P.; Lefranc, M.P.; Pirtskhalava, M. DBAASP: database of antimicrobial activity and structure of peptides. FEMS Microbiol. Lett.,  2014, 357(1), 63-68.
[http://dx.doi.org/10.1111/1574-6968.12489] [PMID:  24888447] 
[30] 
Fan, L.; Sun, J.; Zhou, M.; Zhou, J.; Lao, X.; Zheng, H.; Xu, H. DRAMP: a comprehensive data repository of antimicrobial peptides. Sci. Rep.,  2016, 6, 24482.
[http://dx.doi.org/10.1038/srep24482] [PMID:  27075512] 
[31] 
Zhao, X.; Wu, H.; Lu, H.; Li, G.; Huang, Q. LAMP: A Database Linking Antimicrobial Peptides. PLoS One,  2013, 8(6)e66557
[http://dx.doi.org/10.1371/journal.pone.0066557] [PMID:  23825543] 
[32] 
An, Y.; Wang, J.; Li, C.; Leier, A.; Marquez-Lago, T.; Wilksch, J.; Zhang, Y.; Webb, G.I.; Song, J.; Lithgow, T. Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI. Brief. Bioinform.,  2018, 19(1), 148-161.
[PMID: 27777222] 
[33] 
Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N.D.; Webb, G.I.; Chou, K.C. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform., 2018.
[PMID: 29897410] 
[34] 
Wang, J.; Li, J.; Yang, B.; Xie, R.; Marquez-Lago, T.T.; Leier, A.; Hayashida, M.; Akutsu, T.; Zhang, Y.; Chou, K.C.; Selkrig, J.; Zhou, T.; Song, J.; Lithgow, T. Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics, 2018.
[PMID: 30388198] 
[35] 
Wang, J.; Yang, B.; Leier, A.; Marquez-Lago, T.T.; Hayashida, M.; Rocker, A.; Zhang, Y.; Akutsu, T.; Chou, K.C.; Strugnell, R.A.; Song, J.; Lithgow, T. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics,  2018, 34(15), 2546-2555.
[http://dx.doi.org/10.1093/bioinformatics/bty155] [PMID:  29547915] 
[36] 
Yu, J.; Shi, S.; Zhang, F.; Chen, G.; Cao, M. PredGly: Predicting lysine glycation sites for Homo sapiens based on XGboost feature optimization. Bioinformatics, 2018.
[PMID: 30590442] 
[37] 
Xu, Z.C.; Feng, P.M.; Yang, H.; Qiu, W.R.; Chen, W.; Lin, H. iRNAD: a computational tool for identifying D modification sites in RNA sequence. Bioinformatics,  2019, 35(23), 4922-4929.
[http://dx.doi.org/10.1093/bioinformatics/btz358] [PMID:  31077296] 
[38] 
Wei, L.; Su, R.; Luan, S.; Liao, Z.; Manavalan, B.; Zou, Q.; Shi, X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics,  2019, 35(23), 4930-4937.
[http://dx.doi.org/10.1093/bioinformatics/btz408] [PMID:  31099381] 
[39] 
Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, M.; Behbahani, M.; Mohabatkar, H. Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol.,  2014, 341, 34-40.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.037] [PMID:  24035842] 
[40] 
Akbar, S.; Hayat, M.; Iqbal, M.; Jan, M.A. iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artif. Intell. Med.,  2017, 79, 62-70.
[http://dx.doi.org/10.1016/j.artmed.2017.06.008] [PMID:  28655440] 
[41] 
Wei, L.; Zhou, C.; Su, R.; Zou, Q. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics,  2019, 35(21), 4272-4280.
[http://dx.doi.org/10.1093/bioinformatics/btz246] [PMID:  30994882] 
[42] 
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn.,  1995, 20(3), 273-297.
[http://dx.doi.org/10.1007/BF00994018] 
[43] 
Chen, Z.; Zhao, P.; Li, F.; Marquez-Lago, T.T.; Leier, A.; Revote, J.; Zhu, Y.; Powell, D.R.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Smith, A.I.; Daly, R.J.; Li, J.; Song, J. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief. Bioinform., 2019.bbz041
[http://dx.doi.org/10.1093/bib/bbz041] [PMID:  31067315] 
[44] 
Nikam, R.; Gromiha, M.M. Seq2Feature: a comprehensive web-based feature extraction tool. Bioinformatics,  2019, 35(22), 4797-4799.
[http://dx.doi.org/10.1093/bioinformatics/btz432] [PMID:  31135038] 
[45] 
Win, T.S.; Schaduangrat, N.; Prachayasittikul, V.; Nantasenamat, C.; Shoombuatong, W. PAAP: a web server for predicting antihypertensive activity of peptides. Future Med. Chem.,  2018, 10(15), 1749-1767.
[http://dx.doi.org/10.4155/fmc-2017-0300] [PMID:  30039980] 
[46] 
Hongjaisee, S.; Nantasenamat, C.; Carraway, T.S.; Shoombuatong, W. HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage. Comput. Biol. Chem.,  2019, 80, 419-432.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.05.006] [PMID:  31146118] 
[47] 
Shoombuatong, W.; Schaduangrat, N.; Pratiwi, R.; Nantasenamat, C. THPep: A machine learning-based approach for predicting tumor homing peptides. Comput. Biol. Chem.,  2019, 80, 441-451.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.05.008] [PMID:  31151025] 
[48] 
Manavalan, B.; Shin, T.H.; Lee, G. PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine. Front. Microbiol.,  2018, 9, 476.
[http://dx.doi.org/10.3389/fmicb.2018.00476] [PMID:  29616000] 
[49] 
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-3typeA: Identifying Three Types of Modification at RNA’s Adenosine Sites. Mol. Ther. Nucleic Acids,  2018, 11, 468-474.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID:  29858081] 
[50] 
Feng, C.Q.; Zhang, Z.Y.; Zhu, X.J.; Lin, Y.; Chen, W.; Tang, H.; Lin, H. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics,  2019, 35(9), 1469-1477.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID:  30247625] 
[51] 
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K.C. iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. Mol. Ther. Nucleic Acids,  2017, 7, 155-163.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID:  28624191] 
[52] 
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics,  2018, 34(24), 4196-4204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID:  29931187] 
[53] 
Tang, H.; Zhao, Y.W.; Zou, P.; Zhang, C.M.; Chen, R.; Huang, P.; Lin, H. HBPred: a tool to identify growth hormone-binding proteins. Int. J. Biol. Sci.,  2018, 14(8), 957-964.
[http://dx.doi.org/10.7150/ijbs.24174] [PMID:  29989085] 
[54] 
Yang, H.; Qiu, W.R.; Liu, G.; Guo, F.B.; Chen, W.; Chou, K.C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci.,  2018, 14(8), 883-891.
[http://dx.doi.org/10.7150/ijbs.24616] [PMID:  29989083] 
[55] 
Chen, W.; Lv, H.; Nie, F.; Lin, H. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics,  2019, 35(16), 2796-2800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID:  30624619] 
[56] 
Feng, P.M.; Ding, H.; Chen, W.; Lin, H. Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med.,  2013, 2013530696
[http://dx.doi.org/10.1155/2013/530696] [PMID:  23762187] 
[57] 
Feng, P.M.; Lin, H.; Chen, W. Identification of antioxidants from sequence information using naïve Bayes. Comput. Math. Methods Med., 2013.2013567529
[http://dx.doi.org/10.1155/2013/567529] [PMID:  24062796] 
[58] 
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA N4-methylcytosine Site Prediction Using Effective Feature Representation. Mol. Ther. Nucleic Acids, 2019.
[http://dx.doi.org/10.1016/j.omtn.2019.04.019] 
[59] 
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics, 2018.
[PMID: 30590410] 
[60] 
Qiang, X.; Zhou, C.; Ye, X.; Du, P.F.; Su, R.; Wei, L. CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby091] [PMID:  30239616] 
[61] 
Wei, L.; Hu, J.; Li, F.; Song, J.; Su, R.; Zou, Q. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby107] [PMID:  30383239] 
[62] 
Manavalan, B.; Lee, J.; Lee, J. Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms. PLoS One,  2014, 9(9)e106542
[http://dx.doi.org/10.1371/journal.pone.0106542] [PMID:  25222008] 
[63] 
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest. Front. Pharmacol.,  2018, 9, 276.
[http://dx.doi.org/10.3389/fphar.2018.00276] [PMID:  29636690] 
[64] 
Manavalan, B.; Subramaniyam, S.; Shin, T.H.; Kim, M.O.; Lee, G. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. J. Proteome Res.,  2018, 17(8), 2715-2726.
[http://dx.doi.org/10.1021/acs.jproteome.8b00148] [PMID:  29893128] 
[65] 
Khatun, M.S.; Hasan, M.M.; Kurata, H. PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features. Front. Genet.,  2019, 10, 129.
[http://dx.doi.org/10.3389/fgene.2019.00129] [PMID:  30891059] 
[66] 
Hasan, M.M.; Kurata, H. GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features. PLoS One,  2018, 13(10)e0200283
[http://dx.doi.org/10.1371/journal.pone.0200283] [PMID:  30312302] 
[67] 
Hasan, M.M.; Khatun, M.S.; Mollah, M.N.H.; Yong, C.; Dianjing, G. NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features. Molecules,  2018, 23(7)E1667
[http://dx.doi.org/10.3390/molecules23071667] [PMID:  29987232] 
[68] 
Hasan, M.M.; Guo, D.; Kurata, H. Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information. Mol. Biosyst.,  2017, 13(12), 2545-2550.
[http://dx.doi.org/10.1039/C7MB00491E] [PMID:  28990628] 
[69] 
Peters, B.; Brenner, S.E.; Wang, E.; Slonim, D.; Kann, M.G. Public Library of Science, 2018.
[70] 
Chen, W.; Yang, H.; Feng, P.; Ding, H.; Lin, H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics,  2017, 33(22), 3518-3523.
[http://dx.doi.org/10.1093/bioinformatics/btx479] [PMID:  28961687] 
[71] 
Manavalan, B.; Govindaraj, R.G.; Shin, T.H.; Kim, M.O.; Lee, G. iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction. Front. Immunol.,  2018, 9, 1695.
[http://dx.doi.org/10.3389/fimmu.2018.01695] [PMID:  30100904] 
[72] 
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions. Front. Immunol.,  2018, 9, 1783.
[http://dx.doi.org/10.3389/fimmu.2018.01783] [PMID:  30108593] 
[73] 
Basith, S.; Manavalan, B.; Shin, T.H.; Lee, G. iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree. Comput. Struct. Biotechnol. J.,  2018, 16, 412-420.
[http://dx.doi.org/10.1016/j.csbj.2018.10.007] [PMID:  30425802] 
[74] 
Hasan, M.M.; Khatun, M.S.; Kurata, H. Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites. Cells,  2019, 8(2)E95
[http://dx.doi.org/10.3390/cells8020095] [PMID:  30696115] 
[75] 
Chen, W.; Feng, P.; Liu, T.; Jin, D. Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins. Curr. Drug Metab.,  2019, 20(3), 224-228.
[http://dx.doi.org/10.2174/1389200219666181031105916] [PMID:  30378494] 
[76] 
Feng, P.M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem.,  2013, 442(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID:  23756733] 
[77] 
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. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics,  2018, 34(14), 2499-2502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID:  29528364] 
[78] 
Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K.C. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res.,  2015, 43(W1)W65-71
[http://dx.doi.org/10.1093/nar/gkv458] [PMID:  25958395] 
[79] 
Cao, D.S.; Liang, Y.Z.; Yan, J.; Tan, G.S.; Xu, Q.S.; Liu, S. PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. J. Chem. Inf. Model.,  2013, 53(11), 3086-3096.
[http://dx.doi.org/10.1021/ci400127q] [PMID:  24047419] 
[80] 
Pande, A.; Patiyal, S.; Lathwal, A.; Arora, C.; Kaur, D.; Dhall, A.; Mishra, G.; Kaur, H.; Sharma, N.; Jain, S. Computing wide range of protein/peptide features from their sequence and structure. bioRxiv, 2019.599126
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DOI https://dx.doi.org/10.2174/1389203721666200117171403	Print ISSN 1389-2037
Publisher Name Bentham Science Publisher	Online ISSN 1875-5550
Current Protein & Peptide Science

Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides

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