iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning

References

• C.C. Song, S.C. Zhang, and H. Huang, Choosing a suitable method for the identification of replication origins in microbial genomes, Front. Microbiol. 6 (2015), pp. 1049.
   PubMed Web of Science ®Google Scholar
• S. Waga and B. Stillman, The DNA replication fork in eukaryotic cells, Ann. Rev. Biochem. 67 (1998), pp. 721–751. doi:10.1146/annurev.biochem.67.1.721.
   PubMed Web of Science ®Google Scholar
• E. Ram, A. Kumar, S. Biswas, A. Kumar, S. Chaubey, M.I. Siddiqi, and S. Habib, Nuclear gyrB encodes a functional subunit of the Plasmodium falciparum gyrase that is involved in apicoplast DNA replication, Mol. Biochem. Parasitol. 154 (2007), pp. 30–39. doi:10.1016/j.molbiopara.2007.04.001.
   PubMed Web of Science ®Google Scholar
• G.I. McFadden and D.S. Roos, Apicomplexan plastids as drug targets, Trends Microbiol. 7 (1999), pp. 328–333. doi:10.1016/S0966-842X(99)01547-4.
   PubMed Web of Science ®Google Scholar
• D. Soldati, The apicoplast as a potential therapeutic target in Toxoplasma and other apicomplexan parasites, Parasitol. Today 15 (1999), pp. 5–7.
   PubMedGoogle Scholar
• Y. Lubelsky, H.K. MacAlpine, and D.M. MacAlpine, Genome-wide localization of replication factors, Methods 57 (2012), pp. 187–195. doi:10.1016/j.ymeth.2012.03.022.
   PubMed Web of Science ®Google Scholar
• M.C. Marsolier-Kergoat, Asymmetry indices for analysis and prediction of replication origins in eukaryotic genomes, Plos One 7 (2012), pp. e45050. doi:10.1371/journal.pone.0045050.
   PubMed Web of Science ®Google Scholar
• M. Mechali, Eukaryotic DNA replication origins: Many choices for appropriate answers, Nat. Rev. Mol. Cell Biol. 11 (2010), pp. 728–738. doi:10.1038/nrm2976.
   PubMed Web of Science ®Google Scholar
• C.A. Nieduszynski, Y. Knox, and A.D. Donaldson, Genome-wide identification of replication origins in yeast by comparative genomics, Genes Dev. 20 (2006), pp. 1874–1879. doi:10.1101/gad.385306.
   PubMed Web of Science ®Google Scholar
• W. Chen, P. Feng, and H. Lin, Prediction of replication origins by calculating DNA structural properties, FEBS Lett. 586 (2012), pp. 934–938. doi:10.1016/j.febslet.2012.02.034.
   PubMed Web of Science ®Google Scholar
• W.C. Li, E.Z. Deng, and H. Ding, iORI-PseKNC: A predictor for identifying origin of replication with pseudo k-tuple nucleotide composition, Chemom. Intell. Lab. Syst. 141 (2015), pp. 100–106. doi:10.1016/j.chemolab.2014.12.011.
   Web of Science ®Google Scholar
• F.Y. Dao, H. Lv, F. Wang, C.Q. Feng, H. Ding, and H. Lin, Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique, Bioinformat. 35 (2018), pp. 2075–2083. doi:10.1093/bioinformatics/bty943.
   Google Scholar
• Y.Q. Xing, G.Q. Liu, X.J. Zhao, and L. Cai, Genome-wide characterization and prediction of Arabidopsis thaliana replication origins, Biosyst. 124 (2014), pp. 1–6. doi:10.1016/j.biosystems.2014.07.001.
   Google Scholar
• D.T. Do and N.Q.K. Le, Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features, Genomics 112 (2020), pp. 2445–2451. doi:10.1016/j.ygeno.2020.01.017.
   PubMed Web of Science ®Google Scholar
• F.Y. Dao, H. Lv, H. Zulfiqar, H. Yang, W. Su, H. Gao, H. Ding, and H. Lin, A computational platform to identify origins of replication sites in eukaryotes, Brief. Bioinform. (2020). doi:10.1093/bib/bbaa017.
   Google Scholar
• B. Manavalan, S. Basith, T. Shin, and G. Lee, Computational prediction of species-specific yeast DNA replication origin via iterative feature representation, Brief. Bioinform. (2020). doi:10.1093/bib/bbaa304.
   Google Scholar
• L.Y. Wei, W.J. He, A. Malik, R. Su, L.Z. Cui, and B. Manavalan, Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework, Brief. Bioinform. (2020). doi:10.1093/bib/bbaa275.
   Google Scholar
• C. Chen, Q.M. Zhang, Q. Ma, and B. Yu, LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion, Chemom. Intel. Lab. Syst. 191 (2019), pp. 54–64. doi:10.1016/j.chemolab.2019.06.003.
   Web of Science ®Google Scholar
• F. Amini and G. Hu, A two-layer feature selection method using genetic algorithm and elastic net, Exp. Syst. Appl. 166 (2021), pp. 114072. doi:10.1016/j.eswa.2020.114072.
   Web of Science ®Google Scholar
• T. Oishi, Y. Hayashi, M. Noguchi, F. Yano, S. Kumada, K. Takayama, K. Okada, and Y. Onuki, Creation of novel large dataset comprising several granulation methods and the prediction of tablet properties from critical material attributes and critical process parameters using regularized linear regression models including interaction terms, Int. J. Pharm. 577 (2020), pp. 119083.
   PubMedGoogle Scholar
• K. Takayama, T. Sato, K. Sato, H. Todo, Y. Obata, and K. Sugibayashi, Prediction of tablet characteristics based on sparse modeling for residual stresses simulated by the finite element method incorporating Drucker-Prager cap model, J. Drug Deliv. Sci. Technol. 52 (2019), pp. 1021–1031. doi:10.1016/j.jddst.2019.06.017.
   Web of Science ®Google Scholar
• K.C. Chou, Impacts of bioinformatics to medicinal chemistry, Med. Chem. 11 (2015), pp. 218–234. doi:10.2174/1573406411666141229162834.
   PubMed Web of Science ®Google Scholar
• Q. Zou, G. Lin, X. Jiang, X. Liu, and X. Zeng, Sequence clustering in bioinformatics: An empirical study, Brief. Bioinform. 21 (2020), pp. 1–10.
   Web of Science ®Google Scholar
• H. Lv, F.Y. Dao, Z.X. Guan, D. Zhang, J.X. Tan, Y. Zhang, W. Chen, and H. Lin, iDNA6mA-rice: A computational tool for detecting N6-methyladenine sites in rice, Front. Genet. 10 (2019), pp. 793. doi:10.3389/fgene.2019.00793.
   PubMed Web of Science ®Google Scholar
• Z. Chen, P. Zhao, F.Y. Li, T.T. Marquez-Lago, A. Leier, J. Revote, Y. Zhu, D.R. Powell, T. Akutsu, G.I. Webb, K.C. Chou, A.I. Smith, R.J. Daly, J. Li., and J. Song, iLearn: An integrated platform and meta-learner for feature engineering machine-learning analysis and modeling of DNA, RNA and protein sequence data, Brief. Bioinform. 21 (2020), pp. 1047–1057. doi:10.1093/bib/bbz041.
   PubMed Web of Science ®Google Scholar
• K.C. Chou, Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformat. 21 (2005), pp. 10–19. doi:10.1093/bioinformatics/bth466.
   Google Scholar
• S. Akbar and M. Hayat, iMethyl-STTNC: Identification of N(6) methylade-nosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences, J. Theor. Biol. 455 (2018), pp. 205–211. doi:10.1016/j.jtbi.2018.07.018.
   PubMed Web of Science ®Google Scholar
• M. Arif, M. Hayat, and Z. Jan, iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition, J. Theor. Biol. 442 (2018), pp. 11–21. doi:10.1016/j.jtbi.2018.01.008.
   PubMed Web of Science ®Google Scholar
• E. Contreras-Torres, Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC, J. Theor. Biol. 454 (2018), pp. 139–145. doi:10.1016/j.jtbi.2018.05.033.
   PubMed Web of Science ®Google Scholar
• S. Krishnan, Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains, J. Theor. Biol. 445 (2018), pp. 62–74. doi:10.1016/j.jtbi.2018.02.008.
   PubMed Web of Science ®Google Scholar
• Y.Y. Liang and S.L. Zhang, Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback Leibler divergence, J. Theor. Biol. 454 (2018), pp. 22–29. doi:10.1016/j.jtbi.2018.05.035.
   PubMed Web of Science ®Google Scholar
• J. Mei, Y. Fu, and J. Zhao, Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition, J. Theor. Biol. 456 (2018), pp. 41–48. doi:10.1016/j.jtbi.2018.07.040.
   PubMed Web of Science ®Google Scholar
• W. Chen, T. Lei, D. Jin, H. Lin, and K.C. Chou, PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition, Anal. Biochem. 456 (2014), pp. 53–60. doi:10.1016/j.ab.2014.04.001.
   PubMed Web of Science ®Google Scholar
• W. Chen and H. Lin, Pseudo nucleotide composition or PseKNC: An effective formulation for analyzing genomic sequences, Mol. Biosyst. 11 (2015), pp. 2620–2634. doi:10.1039/C5MB00155B.
   PubMed Web of Science ®Google Scholar
• W. Chen, H. Tang, J. Ye, and H. Lin, iRNA-PseU: Identifying RNA pseudouridine sites, Mol. Ther. – Nucl. Acid. 5 (2016), pp. 332.
   Web of Science ®Google Scholar
• B. Liu, L.Y. Fang, R. Long, X. Lan, and K.C. Zhou, iEnhancer-2L: A two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition, Bioinformat. 32 (2016), pp. 362–369. doi:10.1093/bioinformatics/btv604.
   Google Scholar
• B. Liu, R. Long, and K.C. Chou, iDHS-EL: Identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework, Bioinformat. 32 (2016), pp. 2411–2418. doi:10.1093/bioinformatics/btw186.
   Google Scholar
• B. Liu, S.Y. Wang, and R. Long, iRSpot-EL: Identify recombination spots with an ensemble learning approach, Bioinformat. 33 (2017), pp. 35–41. doi:10.1093/bioinformatics/btw539.
   Google Scholar
• B. Liu, F. Yang, and K.C. Zhou, 2L-piRNA: A two-layer ensemble classifier for identifying piwi interacting RNAs and their function, Mol. Ther. – Nucl. Acids 7 (2017), pp. 267–277. doi:10.1016/j.omtn.2017.04.008.
   PubMedGoogle Scholar
• B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, and K.C. Chou, Pse-in-one: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nucleic Acids Res. 43 (2015), pp. W65–W71. doi:10.1093/nar/gkv458.
   PubMed Web of Science ®Google Scholar
• B. Liu and H. Wu, Pse-in-one 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nat. Sci. 9 (2017), pp. 67–91.
   Google Scholar
• L.C. Zhang and L. Kong, iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components, J. Theor. Biol. 441 (2018), pp. 1–8. doi:10.1016/j.jtbi.2017.12.025.
   PubMed Web of Science ®Google Scholar
• S.L. Zhang, Z.P. Duan, W.H. Yang, C.L. Qian, and Y.W. You, iDHS-DASTS: Identifying DNase I hypersensitive sites based on LASSO and stacking learning, Molec. Omics 17 (2021), pp. 130–141. doi:10.1039/D0MO00115E.
   PubMed Web of Science ®Google Scholar
• P. Gislason, J. Benediktsson, and J. Sveinsson, Random forests for land cover classification, Pattern Recogn. Lett. 27 (2006), pp. 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• X. Li, L. Wang, and E. Sung, AdaBoost with SVM-based component classifiers, Eng Appl. Artif. Intell. 21 (2008), pp. 785–795. doi:10.1016/j.engappai.2007.07.001.
   Web of Science ®Google Scholar
• N. Alexey and K. Alois, Gradient boosting machines, A tutorial, Front Neurorobot. 7 (2013), pp. 21.
   PubMed Web of Science ®Google Scholar
• P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Mach. Learn. 63 (2006), pp. 3–42. doi:10.1007/s10994-006-6226-1.
   Web of Science ®Google Scholar
• J.A.K. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (1999), pp. 293–300. doi:10.1023/A:1018628609742.
   Web of Science ®Google Scholar
• W.Y. He, C.Z. Jia, and Q. Zou, 4mCPred: Machine learning methods for DNAN4-methylcytosine sites prediction, Bioinformat. 35 (2019), pp. 593–601. doi:10.1093/bioinformatics/bty668.
   Google Scholar
• L.Y. Wei, S. Luan, L.A.E. Nagai, R. Su, and Q. Zou, Exploring sequence based features for the improved prediction of DNA N4-methylcytosine sites in multiple species, Bioinformat. 35 (2019), pp. 1326–1333. doi:10.1093/bioinformatics/bty824.
   Google Scholar
• Y.P. Zhang and Q. Zou, PPTPP: A novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning, Bioinformat. 36 (2020), pp. 3982–3987. doi:10.1093/bioinformatics/btaa275.
   Google Scholar
• L.Y. Wei, P.W. Xing, J.J. Tang, and Q. Zou, PhosPred-RF: A novel sequence-based predictor for phosphorylation sites using sequential information only, IEEE Trans. Nanobiosci. 16 (2017), pp. 240–247. doi:10.1109/TNB.2017.2661756.
   PubMed Web of Science ®Google Scholar
• J.S. Wang and S.L. Zhang, PA-PseU: An incremental passive-aggressive based method for identifying RNA pseudouridine sites via Chou’s 5-steps rule, Chemom. Intell. Lab. Syst.210 (2021), pp. 104250. doi:10.1016/j.chemolab.2021.104250.
   Google Scholar
• S.L. Zhang and T. Xue, Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting, Molec Genet. Genom. 295 (2020), pp. 1431–1442. doi:10.1007/s00438-020-01711-8.
   PubMed Web of Science ®Google Scholar
• R. Su, J. Hu, Q. Zou, B. Manavalan, and L.Y. Wei, Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools, Brief. Bioinformatics 124 (2019), pp. 1–13.
   Google Scholar
• J.H. Jia, Z. Liu, X. Xiao, B.X. Liu, and K.C. Chou, iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC, J. Theor. Biol. 377 (2015), pp. 47–56. doi:10.1016/j.jtbi.2015.04.011.
   PubMed Web of Science ®Google Scholar
• S. Basith, B. Manavalan, T.H. Shin, and G. Lee, iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomized tree, Comput. Struct. Biotechnol. J. 16 (2018), pp. 412–420. doi:10.1016/j.csbj.2018.10.007.
   PubMed Web of Science ®Google Scholar
• B. Manavalan, R.G. Govindaraj, T.H. Shin, M.O. Kim, and G. Lee, iBCE-EL: A new ensemble learning framework for improved linear B-cell epitope prediction, Front Immunol. 9 (1695), pp. 2018.
   Google Scholar