Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites in Saccharomyces cerevisiae

Author(s): Rajiv G. Govindaraj, Sathiyamoorthy Subramaniyam, Balachandran Manavalan*

Journal Name: Current Genomics

Volume 21 , Issue 1 , 2020

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Graphical Abstract:


Abstract:

Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved.

Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set.

Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors.

Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

Keywords: Extremely randomized tree, feature optimization, N6-methyladenosine sites, cross-validation, RNA sequences, Saccharomyces cerevisiae.

[1]
Maden, B. The numerous modified nucleotides in eukaryotic ribosomal RNA. Progress in nucleic acid research and molecular biology; Elsevier, 1990, Vol. 39, pp. 241-303.
[2]
Wang, X.; Lu, Z.; Gomez, A.; Hon, G.C.; Yue, Y.; Han, D.; Fu, Y.; Parisien, M.; Dai, Q.; Jia, G.; Ren, B.; Pan, T.; He, C. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature, 2014, 505(7481), 117-120.
[http://dx.doi.org/10.1038/nature12730] [PMID: 24284625]
[3]
Yue, Y.; Liu, J.; He, C. RNA N6-methyladenosine methylation in post-transcriptional gene expression regulation. Genes Dev., 2015, 29(13), 1343-1355.
[http://dx.doi.org/10.1101/gad.262766.115] [PMID: 26159994]
[4]
Wei, C.M.; Gershowitz, A.; Moss, B. 5′-Terminal and internal methylated nucleotide sequences in HeLa cell mRNA. Biochemistry, 1976, 15(2), 397-401.
[http://dx.doi.org/10.1021/bi00647a024] [PMID: 174715]
[5]
Zhong, S.; Li, H.; Bodi, Z.; Button, J.; Vespa, L.; Herzog, M.; Fray, R.G. MTA is an Arabidopsis messenger RNA adenosine methylase and interacts with a homolog of a sex-specific splicing factor. Plant Cell, 2008, 20(5), 1278-1288.
[http://dx.doi.org/10.1105/tpc.108.058883] [PMID: 18505803]
[6]
Bodi, Z.; Button, J.D.; Grierson, D.; Fray, R.G. Yeast targets for mRNA methylation. Nucleic Acids Res., 2010, 38(16), 5327-5335.
[http://dx.doi.org/10.1093/nar/gkq266] [PMID: 20421205]
[7]
Clancy, M.J.; Shambaugh, M.E.; Timpte, C.S.; Bokar, J.A. Induction of sporulation in Saccharomyces cerevisiae leads to the formation of N6-methyladenosine in mRNA: a potential mechanism for the activity of the IME4 gene. Nucleic Acids Res., 2002, 30(20), 4509-4518.
[http://dx.doi.org/10.1093/nar/gkf573] [PMID: 12384598 ]
[8]
Liu, N.; Pan, T. N6-methyladenosine-encoded epitranscriptomics. Nat. Struct. Mol. Biol., 2016, 23(2), 98-102.
[http://dx.doi.org/10.1038/nsmb.3162] [PMID: 26840897]
[9]
Edupuganti, R.R.; Geiger, S.; Lindeboom, R.G.H.; Shi, H.; Hsu, P.J.; Lu, Z.; Wang, S-Y.; Baltissen, M.P.A.; Jansen, P.W.T.C.; Rossa, M.; Müller, M.; Stunnenberg, H.G.; He, C.; Carell, T.; Vermeulen, M.N. 6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis. Nat. Struct. Mol. Biol., 2017, 24(10), 870-878.
[http://dx.doi.org/10.1038/nsmb.3462] [PMID: 28869609]
[10]
Slobodin, B.; Han, R.; Calderone, V.; Vrielink, J. A. O.; Loayza-Puch, F.; Elkon, R.; Agami, R. Transcription impacts the efficiency of mRNA translation via co-transcriptional N6-adenosine methylation Cell, 2017, 169(2), 326-337 e12..
[http://dx.doi.org/10.1016/j.cell.2017.03.031 ]
[11]
Akilzhanova, A.; Nurkina, Z.; Momynaliev, K.; Ramanculov, E.; Zhumadilov, Z.; Rakhypbekov, T.; Hayashida, N.; Nakashima, M.; Takamura, N. Genetic profile and determinants of homocysteine levels in Kazakhstan patients with breast cancer. Anticancer Res., 2013, 33(9), 4049-4059.
[PMID: 24023349]
[12]
Machiela, M.J.; Lindström, S.; Allen, N.E.; Haiman, C.A.; Albanes, D.; Barricarte, A.; Berndt, S.I.; Bueno-de-Mesquita, H.B.; Chanock, S.; Gaziano, J.M.; Gapstur, S.M.; Giovannucci, E.; Henderson, B.E.; Jacobs, E.J.; Kolonel, L.N.; Krogh, V.; Ma, J.; Stampfer, M.J.; Stevens, V.L.; Stram, D.O.; Tjønneland, A.; Travis, R.; Willett, W.C.; Hunter, D.J.; Le Marchand, L.; Kraft, P. Association of type 2 diabetes susceptibility variants with advanced prostate cancer risk in the Breast and Prostate Cancer Cohort Consortium. Am. J. Epidemiol., 2012, 176(12), 1121-1129.
[http://dx.doi.org/10.1093/aje/kws191] [PMID: 23193118]
[13]
Heiliger, K-J.; Hess, J.; Vitagliano, D.; Salerno, P.; Braselmann, H.; Salvatore, G.; Ugolini, C.; Summerer, I.; Bogdanova, T.; Unger, K.; Thomas, G.; Santoro, M.; Zitzelsberger, H. Novel candidate genes of thyroid tumourigenesis identified in Trk-T1 transgenic mice. Endocr. Relat. Cancer, 2012, 19(3), 409-421.
[http://dx.doi.org/10.1530/ERC-11-0387] [PMID: 22454401]
[14]
Zheng, G.; Dahl, J.A.; Niu, Y.; Fedorcsak, P.; Huang, C-M.; Li, C.J.; Vågbø, C.B.; Shi, Y.; Wang, W-L.; Song, S-H.; Lu, Z.; Bosmans, R.P.; Dai, Q.; Hao, Y.J.; Yang, X.; Zhao, W.M.; Tong, W.M.; Wang, X.J.; Bogdan, F.; Furu, K.; Fu, Y.; Jia, G.; Zhao, X.; Liu, J.; Krokan, H.E.; Klungland, A.; Yang, Y.G.; He, C. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell, 2013, 49(1), 18-29.
[http://dx.doi.org/10.1016/j.molcel.2012.10.015] [PMID: 23177736]
[15]
Dominissini, D.; Moshitch-Moshkovitz, S.; Schwartz, S.; Salmon-Divon, M.; Ungar, L.; Osenberg, S.; Cesarkas, K.; Jacob-Hirsch, J.; Amariglio, N.; Kupiec, M.; Sorek, R.; Rechavi, G. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature, 2012, 485(7397), 201-206.
[http://dx.doi.org/10.1038/nature11112] [PMID: 22575960 ]
[16]
Meyer, K.D.; Saletore, Y.; Zumbo, P.; Elemento, O.; Mason, C.E.; Jaffrey, S.R. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell, 2012, 149(7), 1635-1646.
[http://dx.doi.org/10.1016/j.cell.2012.05.003] [PMID: 22608085]
[17]
Keith, G. Mobilities of modified ribonucleotides on two-dimensional cellulose thin-layer chromatography. Biochimie, 1995, 77(1-2), 142-144.
[http://dx.doi.org/10.1016/0300-9084(96)88118-1] [PMID: 7599271]
[18]
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.C. iRNA-Methyl: identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal. Biochem., 2015, 490, 26-33.
[http://dx.doi.org/10.1016/j.ab.2015.08.021] [PMID: 26314792]
[19]
Schwartz, S.; Agarwala, S.D.; Mumbach, M.R.; Jovanovic, M.; Mertins, P.; Shishkin, A.; Tabach, Y.; Mikkelsen, T.S.; Satija, R.; Ruvkun, G.; Carr, S.A.; Lander, E.S.; Fink, G.R.; Regev, A. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell, 2013, 155(6), 1409-1421.
[http://dx.doi.org/10.1016/j.cell.2013.10.047] [PMID: 24269006]
[20]
Chen, W.; Tran, H.; Liang, Z.; Lin, H.; Zhang, L. Identification and analysis of the N(6)-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci. Rep., 2015, 5, 13859.
[http://dx.doi.org/10.1038/srep13859] [PMID: 26343792]
[21]
Liu, Z.; Xiao, X.; Yu, D-J.; Jia, J.; Qiu, W-R.; Chou, K-C. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem., 2016, 497, 60-67.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID: 26748145 ]
[22]
Jia, C-Z.; Zhang, J-J.; Gu, W-Z. RNA-MethylPred: a high-accuracy predictor to identify N6-methyladenosine in RNA. Anal. Biochem., 2016, 510, 72-75.
[http://dx.doi.org/10.1016/j.ab.2016.06.012] [PMID: 27338301]
[23]
Li, G-Q.; Liu, Z.; Shen, H-B.; Yu, D-J. TargetM6A: Identifying N6-methyladenosine sites from RNA sequences via position-specific nucleotide propensities and a support vector machine. IEEE Trans. Nanobioscience, 2016, 15(7), 674-682.
[http://dx.doi.org/10.1109/TNB.2016.2599115] [PMID: 27552763]
[24]
Zhang, M.; Sun, J-W.; Liu, Z.; Ren, M-W.; Shen, H-B.; Yu, D-J. Improving N(6)-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties. Anal. Biochem., 2016, 508, 104-113.
[http://dx.doi.org/10.1016/j.ab.2016.06.001] [PMID: 27293216]
[25]
Chen, W.; Xing, P.; Zou, Q.; Detecting, N. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines. Sci. Rep., 2017, 7, 40242.
[http://dx.doi.org/10.1038/srep40242] [PMID: 28079126]
[26]
Xing, P.; Su, R.; Guo, F.; Wei, L.; Identifying, N. Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine. Sci. Rep., 2017, 7, 46757.
[http://dx.doi.org/10.1038/srep46757] [PMID: 28440291]
[27]
Wei, L.; Chen, H.; Su, R. M6APred-EL: a sequence-based predictor for identifying N6-methyladenosine sites using ensemble learning. Mol. Ther. Nucleic Acids, 2018, 12, 635-644.
[http://dx.doi.org/10.1016/j.omtn.2018.07.004] [PMID: 30081234]
[28]
Chen, W.; Ding, H.; Zhou, X.; Lin, H.; Chou, K-C. iRNA(m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal. Biochem., 2018, 561-562, 59-65.
[http://dx.doi.org/10.1016/j.ab.2018.09.002] [PMID: 30201554]
[29]
Akbar, S.; Hayat, M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol., 2018, 455, 205-211.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.018] [PMID: 30031793 ]
[30]
Huang, Y.; He, N.; Chen, Y.; Chen, Z.; Li, L. BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach. Int. J. Biol. Sci., 2018, 14(12), 1669-1677.
[http://dx.doi.org/10.7150/ijbs.27819] [PMID: 30416381]
[31]
Qiang, X.; Chen, H.; Ye, X.; Su, R.; Wei, L. M6AMRFS: robust prediction of N6-methyladenosine sites with sequence-based features in multiple species. Front. Genet., 2018, 9, 495.
[http://dx.doi.org/10.3389/fgene.2018.00495] [PMID: 30410501]
[32]
Zhuang, Y.Y.; Liu, H.J.; Song, X.; Ju, Y.; Peng, H. A linear regression predictor for identifying N6-methyladenosine sites using frequent gapped K-mer pattern. Mol. Ther. Nucleic Acids, 2019, 18, 673-680.
[http://dx.doi.org/10.1016/j.omtn.2019.10.001] [PMID: 31707204]
[33]
Zhu, X.; He, J.; Zhao, S.; Tao, W.; Xiong, Y.; Bi, S. A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Brief. Funct. Genomics, 2019, 18(6), 367-376.
[http://dx.doi.org/10.1093/bfgp/elz018] [PMID: 31609411]
[34]
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2019, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID: 29360500]
[35]
Dao, F.Y.; Lv, H.; Wang, F.; Feng, C.Q.; Ding, H.; Chen, W.; Lin, H. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics, 2019, 35(12), 2075-2083.
[http://dx.doi.org/10.1093/bioinformatics/bty943] [PMID: 30428009]
[36]
Lou, C.; Zhao, J.; Shi, R.; Wang, Q.; Zhou, W.; Wang, Y.; Wang, G.; Huang, L.; Feng, X.; Zhou, F. sefOri: selecting the best-engineered squence features to predict DNA replication origins. Bioinformatics, 2019, 36(1), 49-55.
[http://dx.doi.org/10.1093/bioinformatics/btz506]
[37]
Tan, J.X.; Li, S.H.; Zhang, Z.M.; Chen, C.X.; Chen, W.; Tang, H.; Lin, H. Identification of hormone binding proteins based on machine learning methods. Math. Biosci. Eng., 2019, 16(4), 2466-2480.
[http://dx.doi.org/10.3934/mbe.2019123] [PMID: 31137222]
[38]
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., 2019.bbz123
[http://dx.doi.org/10.1093/bib/bbz123] [PMID: 31633777]
[39]
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]
[40]
Manavalan, B.; Lee, J. SVMQA: support-vector-machine-based protein single-model quality assessment. Bioinformatics, 2017, 33(16), 2496-2503.
[http://dx.doi.org/10.1093/bioinformatics/btx222] [PMID: 28419290]
[41]
Lai, H.Y.; Zhang, Z.Y.; Su, Z.D.; Su, W.; Ding, H.; Chen, W.; Lin, H. iProEP: A computational predictor for predicting promoter. Mol. Ther. Nucleic Acids, 2019, 17, 337-346.
[http://dx.doi.org/10.1016/j.omtn.2019.05.028] [PMID: 31299595]
[42]
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]
[43]
Ding, H.; Li, D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids, 2015, 47(2), 329-333.
[http://dx.doi.org/10.1007/s00726-014-1862-4] [PMID: 25385313]
[44]
Geurts, P.; Ernst, D.; Wehenkel, L. Extremely randomized trees. Mach. Learn., 2006, 63(1), 3-42.
[http://dx.doi.org/10.1007/s10994-006-6226-1]
[45]
Paul, A.; Furmanchuk, A.; Liao, W.K.; Choudhary, A.; Agrawal, A. Property prediction of organic donor molecules for photovoltaic applications using extremely randomized trees. Mol. Inform., 2019, 38(11-12)e1900038
[http://dx.doi.org/10.1002/minf.201900038] [PMID: 31503423]
[46]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees. Comput. Struct. Biotechnol. J., 2019, 17, 972-981.
[http://dx.doi.org/10.1016/j.csbj.2019.06.024] [PMID: 31372196]
[47]
Nattee, C.; Khamsemanan, N.; Lawtrakul, L.; Toochinda, P.; Hannongbua, S. A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees. J. Mol. Graph. Model., 2017, 71, 13-27.
[http://dx.doi.org/10.1016/j.jmgm.2016.09.010] [PMID: 27835827 ]
[48]
Soltaninejad, M.; Yang, G.; Lambrou, T.; Allinson, N.; Jones, T.L.; Barrick, T.R.; Howe, F.A.; Ye, X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. CARS, 2017, 12(2), 183-203.
[http://dx.doi.org/10.1007/s11548-016-1483-3] [PMID: 27651330 ]
[49]
Xia, B.; Zhang, H.; Li, Q.; Li, T. PETS: a stable and accurate predictor of protein-protein interacting sites based on extremely-randomized trees. IEEE Trans. Nanobioscience, 2015, 14(8), 882-893.
[http://dx.doi.org/10.1109/TNB.2015.2491303] [PMID: 26529772]
[50]
Scalzo, F.; Hamilton, R.; Asgari, S.; Kim, S.; Hu, X. Intracranial hypertension prediction using extremely randomized decision trees. Med. Eng. Phys., 2012, 34(8), 1058-1065.
[http://dx.doi.org/10.1016/j.medengphy.2011.11.010] [PMID: 22401795]
[51]
Marée, R.; Geurts, P.; Wehenkel, L. Random subwindows and extremely randomized trees for image classification in cell biology. BMC Cell Biol., 2007, 8(Suppl. 1), S2.
[http://dx.doi.org/10.1186/1471-2121-8-S1-S2] [PMID: 17634092 ]
[52]
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]
[53]
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]
[54]
Charoenkwan, P.; Kanthawong, S.; Schaduangrat, N.; Yana, J.; Shoombuatong, W. PVPred-SCM: improved prediction and analysis of phage virion proteins using a scoring card method. Cells, 2020, 9(2)E353
[http://dx.doi.org/10.3390/cells9020353] [PMID: 32028709]
[55]
Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. i4mCROSE, a bioinformatics tool for the identification of DNA N4- methylcytosine sites in the Rosaceae genome. Int. J. Biol. Macromol., 2019, S0141-8130(19)38547-2..
[http://dx.doi.org/10.1016/j.ijbiomac.2019.12.009] [PMID: 31805335]
[56]
Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. Prediction of S-nitrosylation sites by integrating support vector machines and random forest. Mol Omics, 2019, 15(6), 451-458.
[http://dx.doi.org/10.1039/C9MO00098D] [PMID: 31710075]
[57]
Su, R.; Hu, J.; Zou, Q.; Manavalan, B.; Wei, L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief. Bioinform., 2019.
[http://dx.doi.org/10.1093/bib/bby124] [PMID: 30649170]
[58]
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]
[59]
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]
[60]
Schaduangrat, N.; Nantasenamat, C.; Prachayasittikul, V.; Shoombuatong, W. ACPred: a computational tool for the prediction and analysis of anticancer peptides. Molecules, 2019, 24(10)E1973
[http://dx.doi.org/10.3390/molecules24101973] [PMID: 31121946]
[61]
Laengsri, V.; Shoombuatong, W.; Adirojananon, W.; Nantasenamat, C.; Prachayasittikul, V.; Nuchnoi, P. ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia. BMC Med. Inform. Decis. Mak., 2019, 19(1), 212.
[http://dx.doi.org/10.1186/s12911-019-0929-2] [PMID: 31699079]
[62]
Laengsri, V.; Nantasenamat, C.; Schaduangrat, N.; Nuchnoi, P.; Prachayasittikul, V.; Shoombuatong, W. TargetAntiAngio: a sequence-based tool for the prediction and analysis of anti-angiogenic peptides. Int. J. Mol. Sci., 2019, 20(12)E2950
[http://dx.doi.org/10.3390/ijms20122950] [PMID: 31212918]
[63]
Charoenkwan, P.; Schaduangrat, N.; Nantasenamat, C.; Piacham, T.; Shoombuatong, W. iQSP: a sequence-based tool for the prediction and analysis of quorum sensing peptides via Chou’s 5-steps rule and informative physicochemical properties. Int. J. Mol. Sci., 2019, 21(1)E75
[http://dx.doi.org/10.3390/ijms21010075] [PMID: 31861928]
[64]
Uchida, T.; Furukawa, M.; Kikawada, T.; Yamazaki, K.; Gohara, K. Intracellular trehalose via transporter TRET1 as a method to cryoprotect CHO-K1 cells. Cryobiology, 2017, 77, 50-57.
[http://dx.doi.org/10.1016/j.cryobiol.2017.05.008] [PMID: 28552273 ]
[65]
Qiao, Y.; Xiong, Y.; Gao, H.; Zhu, X.; Chen, P. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics, 2018, 19(1), 14.
[http://dx.doi.org/10.1186/s12859-018-2009-5] [PMID: 29334889]
[66]
He, J.; Fang, T.; Zhang, Z.; Huang, B.; Zhu, X.; Xiong, Y.; Pse, U.I. PseUI: Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics, 2018, 19(1), 306.
[http://dx.doi.org/10.1186/s12859-018-2321-0] [PMID: 30157750]
[67]
Cao, R.; Freitas, C.; Chan, L.; Sun, M.; Jiang, H.; Chen, Z. ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network. Molecules, 2017, 22(10), 1732.
[http://dx.doi.org/10.3390/molecules22101732] [PMID: 29039790]
[68]
Basith, S.; Manavalan, B.; Shin, T.H.; Lee, G. SDM6A: A web-based integrative machine-learning framework for predicting 6mA sites in the rice genome. Mol. Ther. Nucleic Acids, 2019, 18, 131-141.
[http://dx.doi.org/10.1016/j.omtn.2019.08.011] [PMID: 31542696]
[69]
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]
[70]
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), 1964.
[http://dx.doi.org/10.3390/ijms20081964] [PMID: 31013619]
[71]
Manavalan, B.; Shin, T.H.; Lee, G. DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest. Oncotarget, 2017, 9(2), 1944-1956.
[PMID: 29416743]
[72]
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]
[73]
Xu, Q.; Xiong, Y.; Dai, H.; Kumari, K.M.; Xu, Q.; Ou, H-Y.; Wei, D-Q. PDC-SGB: prediction of effective drug combinations using a stochastic gradient boosting algorithm. J. Theor. Biol., 2017, 417, 1-7.
[http://dx.doi.org/10.1016/j.jtbi.2017.01.019] [PMID: 28099868]
[74]
Xiong, Y.; Wang, Q.; Yang, J.; Zhu, X.; Wei, D.Q. PredT4SE-stack: prediction of bacterial type IV secreted effectors from protein sequences using a stacked ensemble method. Front. Microbiol., 2018, 9, 2571.
[http://dx.doi.org/10.3389/fmicb.2018.02571] [PMID: 30416498]
[75]
Yang, W.; Zhu, X.J.; Huang, J.; Ding, H.; Lin, H. A brief survey of machine learning methods in protein sub-Golgi localization. Curr. Bioinform., 2019, 14, 234-240.
[http://dx.doi.org/10.2174/1574893613666181113131415]
[76]
Ding, H.; Yang, W.; Tang, H.; Feng, P.M.; Huang, J.; Chen, W.; Lin, H. PHYPred: a tool for identifying bacteriophage enzymes and hydrolases. Virol. Sin., 2016, 31(4), 350-352.
[http://dx.doi.org/10.1007/s12250-016-3740-6] [PMID: 27151186]
[77]
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.
[http://dx.doi.org/10.1002/med.21658] [PMID: 31922268]
[78]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Mol. Ther. Nucleic Acids, 2019, 16, 733-744.
[http://dx.doi.org/10.1016/j.omtn.2019.04.019] [PMID: 31146255]
[79]
Wei, L.; Luan, S.; Nagai, L.A.E.; Su, R.; Zou, Q. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics, 2019, 35(8), 1326-1333.
[http://dx.doi.org/10.1093/bioinformatics/bty824] [PMID: 30239627]
[80]
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, 2019, 35(12), 2017-2028.
[http://dx.doi.org/10.1093/bioinformatics/bty914] [PMID: 30388198]
[81]
Zhang, Y.; Yu, S.; Xie, R.; Li, J.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Smith, A.I.; Ge, Z.; Wang, J. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. Bioinformatics, 2019, 1, 9.
[http://dx.doi.org/10.1093/bioinformatics/btz629] [PMID: 31393553]
[82]
Li, F.; Chen, J.; Leier, A.; Marquez-Lago, T.; Liu, Q.; Wang, Y.; Revote, J.; Smith, A.I.; Akutsu, T.; Webb, G.I.; Kurgan, L.; Song, J. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics, 2019, •••btz721
[http://dx.doi.org/10.1093/bioinformatics/btz721] [PMID: 31566664]
[83]
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, 2019, 35(16), 2757-2765.
[PMID: 30590410]
[84]
Yu, B.; Qiu, W.; Chen, C.; Ma, A.; Jiang, J.; Zhou, H.; Ma, Q. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics, 2019.btz734
[http://dx.doi.org/10.1093/bioinformatics/btz734] [PMID: 31603468]
[85]
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]


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VOLUME: 21
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Year: 2020
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DOI: 10.2174/1389202921666200219125625
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