Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

General Review Article

Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites

Author(s): Shaherin Basith, Hye Jin Chang, Saraswathy Nithiyanandam, Tae Hwan Shin, Balachandran Manavalan* and Gwang Lee*

Volume 29 , Issue 2 , 2022

Published on: 02 September, 2021

Page: [235 - 250] Pages: 16

DOI: 10.2174/0929867328999210902125308

Price: $65

Abstract

Acetylation on lysine residues is considered one of the most potent protein post-translational modifications, owing to its crucial role in cellular metabolism and regulatory processes. Recent advances in experimental techniques have unraveled several lysine acetylation substrates and sites. However, owing to its cost-ineffectiveness, cumbersome process, time-consumption, and labor-intensiveness, several efforts have been geared towards the development of computational tools. In particular, machine learning (ML)-based approaches hold great promise in the rapid discovery of lysine acetylation modification sites, which could be witnessed by the growing number of prediction tools. Recently, several ML methods have been developed for the prediction of lysine acetylation sites, owing to their time- and cost-effectiveness. In this review, we present a complete survey of the state-of-the-art ML predictors for lysine acetylation. We discuss a variety of key aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, validation techniques, and software utility. Initially, we review lysine acetylation site databases, current ML approaches, working principles, and their performances. Lastly, we discuss the shortcomings and future directions of ML approaches in the prediction of lysine acetylation sites. This review may act as a useful guide for the experimentalists in choosing the right ML tool for their research. Moreover, it may help bioinformaticians in the development of more accurate and advanced MLbased predictors in protein research.

Keywords: Protein, post-translational modification, lysine, acetylation, machine learning, feature encoding, prediction model.

[1]
Wang, Z.A.; Cole, P.A. The chemical biology of reversible lysine post-translational modifications. Cell Chem. Biol., 2020, 27(8), 953-969.
[http://dx.doi.org/10.1016/j.chembiol.2020.07.002] [PMID: 32698016]
[2]
Audagnotto, M.; Dal Peraro, M. Protein post-translational modifications: In silico prediction tools and molecular modeling. Comput. Struct. Biotechnol. J., 2017, 15, 307-319.
[http://dx.doi.org/10.1016/j.csbj.2017.03.004] [PMID: 28458782]
[3]
Wu, M.; Yang, Y.; Wang, H.; Xu, Y. A deep learning method to more accurately recall known lysine acetylation sites. BMC Bioinformatics, 2019, 20(1), 49.
[http://dx.doi.org/10.1186/s12859-019-2632-9] [PMID: 30674277]
[4]
Khoury, G.A.; Baliban, R.C.; Floudas, C.A. Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Sci. Rep., 2011, 1, 1.
[http://dx.doi.org/10.1038/srep00090] [PMID: 22034591]
[5]
Hornbeck, P.V.; Kornhauser, J.M.; Tkachev, S.; Zhang, B.; Skrzypek, E.; Murray, B.; Latham, V.; Sullivan, M. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res., 2012, 40(Database issue), D261-D270.
[http://dx.doi.org/10.1093/nar/gkr1122] [PMID: 22135298]
[6]
Glozak, M.A.; Sengupta, N.; Zhang, X.; Seto, E. Acetylation and deacetylation of non-histone proteins. Gene, 2005, 363, 15-23.
[http://dx.doi.org/10.1016/j.gene.2005.09.010] [PMID: 16289629]
[7]
Polevoda, B.; Sherman, F. Nalpha -terminal acetylation of eukaryotic proteins. J. Biol. Chem., 2000, 275(47), 36479-36482.
[http://dx.doi.org/10.1074/jbc.R000023200] [PMID: 11013267]
[8]
Yang, X.J. The diverse superfamily of lysine acetyltransferases and their roles in leukemia and other diseases. Nucleic Acids Res., 2004, 32(3), 959-976.
[http://dx.doi.org/10.1093/nar/gkh252] [PMID: 14960713]
[9]
Polevoda, B.; Sherman, F. The diversity of acetylated proteins.Genome Biol, , 2002, 3(5)reviews0006..
[10]
Cohen, H.Y.; Lavu, S.; Bitterman, K.J.; Hekking, B.; Imahiyerobo, T.A.; Miller, C.; Frye, R.; Ploegh, H.; Kessler, B.M.; Sinclair, D.A. Acetylation of the C terminus of Ku70 by CBP and PCAF controls Bax-mediated apoptosis. Mol. Cell, 2004, 13(5), 627-638.
[http://dx.doi.org/10.1016/S1097-2765(04)00094-2] [PMID: 15023334]
[11]
Murr, R.; Loizou, J.I.; Yang, Y.G.; Cuenin, C.; Li, H.; Wang, Z.Q.; Herceg, Z. Histone acetylation by Trrap-Tip60 modulates loading of repair proteins and repair of DNA double-strand breaks. Nat. Cell Biol., 2006, 8(1), 91-99.
[http://dx.doi.org/10.1038/ncb1343] [PMID: 16341205]
[12]
Bannister, A.J.; Miska, E.A.; Görlich, D.; Kouzarides, T. Acetylation of importin-alpha nuclear import factors by CBP/p300. Curr. Biol., 2000, 10(8), 467-470.
[http://dx.doi.org/10.1016/S0960-9822(00)00445-0] [PMID: 10801418]
[13]
Brunet, A.; Sweeney, L.B.; Sturgill, J.F.; Chua, K.F.; Greer, P.L.; Lin, Y.; Tran, H.; Ross, S.E.; Mostoslavsky, R.; Cohen, H.Y.; Hu, L.S.; Cheng, H.L.; Jedrychowski, M.P.; Gygi, S.P.; Sinclair, D.A.; Alt, F.W.; Greenberg, M.E. Stress-dependent regulation of FOXO transcription factors by the SIRT1 deacetylase. Science, 2004, 303(5666), 2011-2015.
[http://dx.doi.org/10.1126/science.1094637] [PMID: 14976264]
[14]
Ning, Q.; Yu, M.; Ji, J.; Ma, Z.; Zhao, X. Analysis and prediction of human acetylation using a cascade classifier based on support vector machine. BMC Bioinformatics, 2019, 20(1), 346.
[http://dx.doi.org/10.1186/s12859-019-2938-7] [PMID: 31208321]
[15]
Jeong, H.; Then, F.; Melia, T.J., Jr; Mazzulli, J.R.; Cui, L.; Savas, J.N.; Voisine, C.; Paganetti, P.; Tanese, N.; Hart, A.C.; Yamamoto, A.; Krainc, D. Acetylation targets mutant huntingtin to autophagosomes for degradation. Cell, 2009, 137(1), 60-72.
[http://dx.doi.org/10.1016/j.cell.2009.03.018] [PMID: 19345187]
[16]
Geng, H.; Harvey, C.T.; Pittsenbarger, J.; Liu, Q.; Beer, T.M.; Xue, C.; Qian, D.Z. HDAC4 protein regulates HIF1α protein lysine acetylation and cancer cell response to hypoxia. J. Biol. Chem., 2011, 286(44), 38095-38102.
[http://dx.doi.org/10.1074/jbc.M111.257055] [PMID: 21917920]
[17]
Iyer, A.; Fairlie, D.P.; Brown, L. Lysine acetylation in obesity, diabetes and metabolic disease. Immunol. Cell Biol., 2012, 90(1), 39-46.
[http://dx.doi.org/10.1038/icb.2011.99] [PMID: 22083525]
[18]
Umlauf, D.; Goto, Y.; Feil, R. Site-specific analysis of histone methylation and acetylation. Methods Mol. Biol., 2004, 287, 99-120.
[http://dx.doi.org/10.1385/1-59259-828-5:099] [PMID: 15273407]
[19]
Zhou, H.; Ranish, J.A.; Watts, J.D.; Aebersold, R. Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry. Nat. Biotechnol., 2002, 20(5), 512-515.
[http://dx.doi.org/10.1038/nbt0502-512] [PMID: 11981568]
[20]
Welsch, D.J.; Nelsestuen, G.L. Amino-terminal alanine functions in a calcium-specific process essential for membrane binding by prothrombin fragment 1. Biochemistry, 1988, 27(13), 4939-4945.
[http://dx.doi.org/10.1021/bi00413a052] [PMID: 3167022]
[21]
Wheeler, D.L.; Chappey, C.; Lash, A.E.; Leipe, D.D.; Madden, T.L.; Schuler, G.D.; Tatusova, T.A.; Rapp, B.A. Database resources of the national center for biotechnology information. Nucleic Acids Res., 2000, 28(1), 10-14.
[http://dx.doi.org/10.1093/nar/28.1.10] [PMID: 10592169]
[22]
Keshava Prasad, T.S.; Goel, R.; Kandasamy, K.; Keerthikumar, S.; Kumar, S.; Mathivanan, S.; Telikicherla, D.; Raju, R.; Shafreen, B.; Venugopal, A.; Balakrishnan, L.; Marimuthu, A.; Banerjee, S.; Somanathan, D.S.; Sebastian, A.; Rani, S.; Ray, S.; Harrys Kishore, C.J.; Kanth, S.; Ahmed, M.; Kashyap, M.K.; Mohmood, R.; Ramachandra, Y.L.; Krishna, V.; Rahiman, B.A.; Mohan, S.; Ranganathan, P.; Ramabadran, S.; Chaerkady, R.; Pandey, A. Human protein reference database-2009 update. Nucleic Acids Res., 2009, 37(Database issue), D767-D772.
[http://dx.doi.org/10.1093/nar/gkn892] [PMID: 18988627]
[23]
Peri, S.; Navarro, J.D.; Amanchy, R.; Kristiansen, T.Z.; Jonnalagadda, C.K.; Surendranath, V.; Niranjan, V.; Muthusamy, B.; Gandhi, T.K.; Gronborg, M.; Ibarrola, N.; Deshpande, N.; Shanker, K.; Shivashankar, H.N.; Rashmi, B.P.; Ramya, M.A.; Zhao, Z.; Chandrika, K.N.; Padma, N.; Harsha, H.C.; Yatish, A.J.; Kavitha, M.P.; Menezes, M.; Choudhury, D.R.; Suresh, S.; Ghosh, N.; Saravana, R.; Chandran, S.; Krishna, S.; Joy, M.; Anand, S.K.; Madavan, V.; Joseph, A.; Wong, G.W.; Schiemann, W.P.; Constantinescu, S.N.; Huang, L.; Khosravi-Far, R.; Steen, H.; Tewari, M.; Ghaffari, S.; Blobe, G.C.; Dang, C.V.; Garcia, J.G.; Pevsner, J.; Jensen, O.N.; Roepstorff, P.; Deshpande, K.S.; Chinnaiyan, A.M.; Hamosh, A.; Chakravarti, A.; Pandey, A. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res., 2003, 13(10), 2363-2371.
[http://dx.doi.org/10.1101/gr.1680803] [PMID: 14525934]
[24]
Gnad, F.; Gunawardena, J.; Mann, M. PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res., 2011, 39(Database issue), D253-D260.
[http://dx.doi.org/10.1093/nar/gkq1159] [PMID: 21081558]
[25]
Boutet, E.; Lieberherr, D.; Tognolli, M.; Schneider, M.; Bansal, P.; Bridge, A.J.; Poux, S.; Bougueleret, L.; Xenarios, I. UniProtKB/Swiss-Prot, the manually annotated section of the uniprot knowledgebase: How to use the entry view. Methods Mol. Biol., 2016, 1374, 23-54.
[http://dx.doi.org/10.1007/978-1-4939-3167-5_2] [PMID: 26519399]
[26]
Li, H.; Xing, X.; Ding, G.; Li, Q.; Wang, C.; Xie, L.; Zeng, R.; Li, Y. SysPTM: a systematic resource for proteomic research on post-translational modifications. Mol. Cell. Proteomics, 2009, 8(8), 1839-1849.
[http://dx.doi.org/10.1074/mcp.M900030-MCP200] [PMID: 19366988]
[27]
Lee, T.Y.; Hsu, J.B.; Lin, F.M.; Chang, W.C.; Hsu, P.C.; Huang, H.D. N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites. J. Comput. Chem., 2010, 31(15), 2759-2771.
[http://dx.doi.org/10.1002/jcc.21569] [PMID: 20839302]
[28]
Liu, Z.; Cao, J.; Gao, X.; Zhou, Y.; Wen, L.; Yang, X.; Yao, X.; Ren, J.; Xue, Y. CPLA 1.0: an integrated database of protein lysine acetylation. Nucleic Acids Res., 2011, 39(Database issue), D1029-D1034.
[http://dx.doi.org/10.1093/nar/gkq939] [PMID: 21059677]
[29]
Wang, L.; Du, Y.; Lu, M.; Li, T. ASEB: a web server for KAT-specific acetylation site prediction. Nucleic Acids Res, 2012, 40(Web Server issue), W376-W379.,
[http://dx.doi.org/10.1093/nar/gks437]
[30]
Liu, Z.; Wang, Y.; Gao, T.; Pan, Z.; Cheng, H.; Yang, Q.; Cheng, Z.; Guo, A.; Ren, J.; Xue, Y. CPLM: a database of protein lysine modifications. Nucleic Acids Res., 2014, 42(Database issue), D531-D536.
[http://dx.doi.org/10.1093/nar/gkt1093] [PMID: 24214993]
[31]
Huang, K.Y.; Lee, T.Y.; Kao, H.J.; Ma, C.T.; Lee, C.C.; Lin, T.H.; Chang, W.C.; Huang, H.D. dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications. Nucleic Acids Res., 2019, 47(D1), D298-D308.
[http://dx.doi.org/10.1093/nar/gky1074] [PMID: 30418626]
[32]
Huang, K.Y.; Su, M.G.; Kao, H.J.; Hsieh, Y.C.; Jhong, J.H.; Cheng, K.H.; Huang, H.D.; Lee, T.Y. dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res., 2016, 44(D1), D435-D446.
[http://dx.doi.org/10.1093/nar/gkv1240] [PMID: 26578568]
[33]
Xu, H.; Zhou, J.; Lin, S.; Deng, W.; Zhang, Y.; Xue, Y. PLMD: An updated data resource of protein lysine modifications. J. Genet. Genomics, 2017, 44(5), 243-250.
[http://dx.doi.org/10.1016/j.jgg.2017.03.007] [PMID: 28529077]
[34]
Li, A.; Xue, Y.; Jin, C.; Wang, M.; Yao, X. Prediction of Nepsilon-acetylation on internal lysines implemented in bayesian discriminant method. Biochem. Biophys. Res. Commun., 2006, 350(4), 818-824.
[http://dx.doi.org/10.1016/j.bbrc.2006.08.199] [PMID: 17045240]
[35]
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]
[36]
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]
[37]
Li, S.; Li, H.; Li, M.; Shyr, Y.; Xie, L.; Li, Y. Improved prediction of lysine acetylation by support vector machines. Protein Pept. Lett., 2009, 16(8), 977-983.
[http://dx.doi.org/10.2174/092986609788923338] [PMID: 19689425]
[38]
Xu, Y.; Wang, X.B.; Ding, J.; Wu, L.Y.; Deng, N.Y. Lysine acetylation sites prediction using an ensemble of support vector machine classifiers. J. Theor. Biol., 2010, 264(1), 130-135.
[http://dx.doi.org/10.1016/j.jtbi.2010.01.013] [PMID: 20085770]
[39]
Shao, J.; Xu, D.; Hu, L.; Kwan, Y.W.; Wang, Y.; Kong, X.; Ngai, S.M. Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation. Mol. Biosyst., 2012, 8(11), 2964-2973.
[http://dx.doi.org/10.1039/c2mb25251a] [PMID: 22936054]
[40]
Shi, S.P.; Qiu, J.D.; Sun, X.Y.; Suo, S.B.; Huang, S.Y.; Liang, R.P. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features. Mol. Biosyst., 2012, 8(5), 1520-1527.
[http://dx.doi.org/10.1039/c2mb05502c] [PMID: 22402705]
[41]
Suo, S.B.; Qiu, J.D.; Shi, S.P.; Sun, X.Y.; Huang, S.Y.; Chen, X.; Liang, R.P. Position-specific analysis and prediction for protein lysine acetylation based on multiple features. PLoS One, 2012, 7(11)e49108
[http://dx.doi.org/10.1371/journal.pone.0049108] [PMID: 23173045]
[42]
Suo, S.B.; Qiu, J.D.; Shi, S.P.; Chen, X.; Huang, S.Y.; Liang, R.P. Proteome-wide analysis of amino acid variations that influence protein lysine acetylation. J. Proteome Res., 2013, 12(2), 949-958.
[http://dx.doi.org/10.1021/pr301007j] [PMID: 23298314]
[43]
Hou, T.; Zheng, G.; Zhang, P.; Jia, J.; Li, J.; Xie, L.; Wei, C.; Li, Y. LAceP: lysine acetylation site prediction using logistic regression classifiers. PLoS One, 2014, 9(2)e89575
[http://dx.doi.org/10.1371/journal.pone.0089575] [PMID: 24586884]
[44]
Lu, C.T.; Lee, T.Y.; Chen, Y.J.; Chen, Y.J. An intelligent system for identifying acetylated lysine on histones and nonhistone proteins. BioMed Res. Int., 2014, 2014528650
[http://dx.doi.org/10.1155/2014/528650] [PMID: 25147802]
[45]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics, 2016, 32(20), 3116-3123.
[http://dx.doi.org/10.1093/bioinformatics/btw380] [PMID: 27334473]
[46]
Li, Y.; Wang, M.; Wang, H.; Tan, H.; Zhang, Z.; Webb, G.I.; Song, J. Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features. Sci. Rep., 2014, 4, 5765.
[http://dx.doi.org/10.1038/srep05765] [PMID: 25042424]
[47]
Wuyun, Q.; Zheng, W.; Zhang, Y.; Ruan, J.; Hu, G. Improved species-specific lysine acetylation site prediction based on a large variety of features set. PLoS One, 2016, 11(5)e0155370
[http://dx.doi.org/10.1371/journal.pone.0155370] [PMID: 27183223]
[48]
Bao, W.; Jiang, Z.; Han, K.; Huang, D-S. Prediction of lysine acetylation sites based on neural network.In: Intelligent Computing Theories and Application 2016, Part II, LNCS 9772; Huang, D.-S.A.J.; K.-H., Eds.; Springer International Publishing : Switzerland,; , 2016, pp. 873-879.
[49]
Chen, G.; Cao, M.; Luo, K.; Wang, L.; Wen, P.; Shi, S. ProAcePred: prokaryote lysine acetylation sites prediction based on elastic net feature optimization. Bioinformatics, 2018, 34(23), 3999-4006.
[http://dx.doi.org/10.1093/bioinformatics/bty444] [PMID: 29868863]
[50]
Chen, G.; Cao, M.; Yu, J.; Guo, X.; Shi, S. Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC. J. Theor. Biol., 2019, 461, 92-101.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.047] [PMID: 30365945]
[51]
Westbrook, J.; Feng, Z.; Jain, S.; Bhat, T.N.; Thanki, N.; Ravichandran, V.; Gilliland, G.L.; Bluhm, W.; Weissig, H.; Greer, D.S.; Bourne, P.E.; Berman, H.M. The protein data bank: unifying the archive. Nucleic Acids Res., 2002, 30(1), 245-248.
[http://dx.doi.org/10.1093/nar/30.1.245] [PMID: 11752306]
[52]
Xiu, Q.; Li, D.; Li, H.; Wang, N.; Ding, C. Prediction method for lysine acetylation sites based on LSTM network. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 2019, pp. 179-182.,
[53]
Yu, B.; Yu, Z.; Chen, C.; Ma, A.; Liu, B.; Tian, B.; Ma, Q. DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion. Chemom. Intell. Lab. Syst., 2020.103999
[http://dx.doi.org/10.1016/j.chemolab.2020.103999]
[54]
Wei, L.; He, W.; Malik, A.; Su, R.; Cui, L.; Manavalan, B. Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.Brief. Bioinform.,, 2021, 22(4)bbaa275..
[http://dx.doi.org/10.1093/bib/bbaa275] [PMID: 33152766]
[55]
Tang, Q.; Nie, F.; Kang, J.; Chen, W. mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy. Mol. Ther., 2021, 29(8), 2617-2623.
[http://dx.doi.org/10.1016/j.ymthe.2021.04.004] [PMID: 33823302]
[56]
Hasan, M.M.; Shoombuatong, W.; Kurata, H.; Manavalan, B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools. Brief. Funct. Genomics, 2021, 20(4), 258-272.
[http://dx.doi.org/10.1093/bfgp/elaa028] [PMID: 33491072]
[57]
Hasan, M.M.; Alam, M.A.; Shoombuatong, W.; Deng, H.W.; Manavalan, B.; Kurata, H. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.Brief. Bioinform.,, 2021.bbab167..
[http://dx.doi.org/10.1093/bib/bbab167] [PMID: 33975333]
[58]
Charoenkwan, P.; Nantasenamat, C.; Hasan, M.M.; Manavalan, B.; Shoombuatong, W. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)- based model for improving the prediction of bitter peptides. Bioinformatics, 2021, btab133.,
[http://dx.doi.org/10.1093/bioinformatics/btab133] [PMID: 33638635]
[59]
Charoenkwan, P.; Chiangjong, W.; Nantasenamat, C.; Hasan, M.M.; Manavalan, B.; Shoombuatong, W. StackIL6: astacking ensemble model for improving the prediction of IL-6 inducing peptides. Brief. Bioinform., 2021, bbab172.,
[http://dx.doi.org/10.1093/bib/bbab172] [PMID: 33963832]
[60]
Cai, L.; Ren, X.; Fu, X.; Peng, L.; Gao, M.; Zeng, X. iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor. Bioinformatics, 2021, 37(8), 1060-1067.
[http://dx.doi.org/10.1093/bioinformatics/btaa914] [PMID: 33119044]
[61]
Su, R.; Hu, J.; Zou, Q.; Manavalan, B.; Wei, L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief. Bioinform., 2020, 21(2), 408-420.
[http://dx.doi.org/10.1093/bib/bby124] [PMID: 30649170]
[62]
Hasan, M.M.; Schaduangrat, N.; Basith, S.; Lee, G.; Shoombuatong, W.; Manavalan, B. HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics, 2020, 36(11), 3350-3356.
[http://dx.doi.org/10.1093/bioinformatics/btaa160] [PMID: 32145017]
[63]
Hasan, M.M.; Basith, S.; Khatun, M.S.; Lee, G.; Manavalan, B.; Kurata, H. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform., 2020.
[PMID: 32910169]
[64]
Govindaraj, R.G.; Subramaniyam, S.; Manavalan, B. Extremely-randomized-tree-based prediction of N6-methyla-denosine sites in Saccharomyces cerevisiae. Curr. Genomics, 2020, 21(1), 26-33.
[http://dx.doi.org/10.2174/1389202921666200219125625] [PMID: 32655295]
[65]
Basith, S.; Manavalan, B.; Hwan Shin, T.; Lee, G. Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening. Med. Res. Rev., 2020, 40(4), 1276-1314.
[http://dx.doi.org/10.1002/med.21658] [PMID: 31922268]
[66]
Charoenkwan, P.; Kanthawong, S.; Nantasenamat, C.; Hasan, M.M.; Shoombuatong, W. iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides. Genomics, 2021, 113(1 Pt 2), 689-698.
[http://dx.doi.org/10.1016/j.ygeno.2020.09.065] [PMID: 33017626]
[67]
Charoenkwan, P.; Nantasenamat, C.; Hasan, M.M.; Shoombuatong, W. Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. J. Comput. Aided Mol. Des., 2020, 34(10), 1105-1116.
[http://dx.doi.org/10.1007/s10822-020-00323-z] [PMID: 32557165]
[68]
Charoenkwan, P.; Yana, J.; Nantasenamat, C.; Hasan, M.M.; Shoombuatong, W. iUmami-SCM: A novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides. J. Chem. Inf. Model., 2020, 60(12), 6666-6678.
[http://dx.doi.org/10.1021/acs.jcim.0c00707] [PMID: 33094610]
[69]
Dao, F.Y.; Lv, H.; Su, W.; Sun, Z.J.; Huang, Q.L.; Lin, H. iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network. Brief. Bioinform., 2021, bbab047.,
[http://dx.doi.org/10.1093/bib/bbab047] [PMID: 33751027]
[70]
Dao, F.Y.; Lv, H.; Zhang, D.; Zhang, Z.M.; Liu, L.; Lin, H. DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.Brief. Bioinform.,, 2021, 22(4)bbaa356..
[http://dx.doi.org/10.1093/bib/bbaa356] [PMID: 33279983]
[71]
Lv, H.; Dao, F.Y.; Guan, Z.X.; Yang, H.; Li, Y.W.; Lin, H. Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method.Brief. Bioinform.,, 2021, 22(4)bbaa255..
[http://dx.doi.org/10.1093/bib/bbaa255] [PMID: 33099604]
[72]
Lv, H.; Dao, F.Y.; Zulfiqar, H.; Lin, H. DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach. Brief. Bioinform., 2021, bbab244.,
[PMID: 34184738]
[73]
Malik, A.A.; Phanus-Umporn, C.; Schaduangrat, N.; Shoombuatong, W.; Isarankura-Na-Ayudhya, C.; Nantasenamat, C. HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors. J. Comput. Chem., 2020, 41(20), 1820-1834.
[http://dx.doi.org/10.1002/jcc.26223] [PMID: 32449536]
[74]
Su, W.; Liu, M.L.; Yang, Y.H.; Wang, J.S.; Li, S.H.; Lv, H.; Dao, F.Y.; Yang, H.; Lin, H. PPD: A manually curated database for experimentally verified prokaryotic promoters. J. Mol. Biol., 2021, 433(11)166860
[http://dx.doi.org/10.1016/j.jmb.2021.166860] [PMID: 33539888]
[75]
Yang, H.; Yang, W.; Dao, F.Y.; Lv, H.; Ding, H.; Chen, W.; Lin, H. A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae. Brief. Bioinform., 2020, 21(5), 1568-1580.
[http://dx.doi.org/10.1093/bib/bbz123] [PMID: 31633777]
[76]
Zhang, D.; Xu, Z.C.; Su, W.; Yang, Y.H.; Lv, H.; Yang, H.; Lin, H. iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features. Bioinformatics, 2021, 37(2), 171-177.
[http://dx.doi.org/10.1093/bioinformatics/btaa702] [PMID: 32766811]
[77]
Wang, D.; Liang, Y.; Xu, D. Capsule network for protein post-translational modification site prediction. Bioinformatics, 2019, 35(14), 2386-2394.
[http://dx.doi.org/10.1093/bioinformatics/bty977] [PMID: 30520972]
[78]
Libbrecht, M.W.; Noble, W.S. Machine learning applications in genetics and genomics. Nat. Rev. Genet., 2015, 16(6), 321-332.
[http://dx.doi.org/10.1038/nrg3920] [PMID: 25948244]
[79]
Cortés, A.J.; Restrepo-Montoya, M.; Bedoya-Canas, L.E. Modern strategies to assess and breed forest tree adaptation to changing climate. Front. Plant Sci., 2020, 11583323
[http://dx.doi.org/10.3389/fpls.2020.583323] [PMID: 33193532]
[80]
Tong, H.; Nikoloski, Z. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. J. Plant Physiol., 2021, 257153354
[http://dx.doi.org/10.1016/j.jplph.2020.153354] [PMID: 33385619]
[81]
Ma, C.; Zhang, H.H.; Wang, X. Machine learning for Big Data analytics in plants. Trends Plant Sci., 2014, 19(12), 798-808.
[http://dx.doi.org/10.1016/j.tplants.2014.08.004] [PMID: 25223304]
[82]
Tan, B.; Grattapaglia, D.; Martins, G.S.; Ferreira, K.Z.; Sundberg, B.; Ingvarsson, P.K. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol., 2017, 17(1), 110.
[http://dx.doi.org/10.1186/s12870-017-1059-6] [PMID: 28662679]
[83]
Reyes-Herrera, P.H.; Muñoz-Baena, L.; Velásquez-Zapata, V.; Patiño, L.; Delgado-Paz, O.A.; Díaz-Diez, C.A.; Navas-Arboleda, A.A.; Cortés, A.J. Inheritance of rootstock effects in avocado (Persea americana Mill.) cv. Hass. Front. Plant Sci., 2020, 11555071
[http://dx.doi.org/10.3389/fpls.2020.555071] [PMID: 33424874]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy