Skip to main content

Advertisement

SpringerLink
Log in

iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition

  • Original Article
  • Published:
Molecular Genetics and Genomics Aims and scope Submit manuscript

Abstract

Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model “iNuc-ext-PseTNC” is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S1, S2, and S3, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ahmad J, Javed F, Hayat M (2017) Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods. Artif Intell Med 78:14–22

    Article  PubMed  Google Scholar 

  • Athey BD, Smith MF, Rankert DA, Williams SP, Langmore JP (1990) The diameters of frozen-hydrated chromatin fibers increase with DNA linker length: evidence in support of variable diameter models for chromatin. J Cell Biol 111:795–806

    Article  CAS  PubMed  Google Scholar 

  • Awazu A (2017) Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition. Bioinformatics 33:42–48

    Article  CAS  PubMed  Google Scholar 

  • Berbenetz NM, Nislow C, Brown GW (2010) Diversity of eukaryotic DNA replication origins revealed by genome-wide analysis of chromatin structure. PLoS Genet 6:e1001092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cao D-S, Xu Q-S, Liang Y-Z (2013) Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962

    Article  CAS  PubMed  Google Scholar 

  • Che Y, Ju Y, Xuan P, Long R, Xing F (2016) Identification of multi-functional enzyme with multi-label classifier. PLoS One 11:e0153503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen Y-K, Li K-B (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition. J Theor Biol 318:1–12

    Article  CAS  PubMed  Google Scholar 

  • Chen W, Feng P-M, Lin H, Chou K-C (2013a) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41(6):e68

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen W, Feng P, Lin H, Chou K (2013b) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res gks1450

  • Chen W, Lei T-Y, Jin D-C, Lin H, Chou K-C (2014) PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456:53–60

    Article  CAS  PubMed  Google Scholar 

  • Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou K-C (2015) PseKNC-general: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31:119–120

    Article  CAS  PubMed  Google Scholar 

  • Chen W, Ding H, Feng P, Lin H, Chou K-C (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7:16895

    PubMed  PubMed Central  Google Scholar 

  • Chen W, Feng P, Yang H, Ding H, Lin H, Chou K-C (2017) iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 8:4208

    PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou K-C (2017a) pLoc-mGneg: predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110:231–239

    Article  CAS  Google Scholar 

  • Cheng X, Xiao X, Chou K-C (2017b) pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34:1448–1456

    Article  CAS  Google Scholar 

  • Cheng X, Xiao X, Chou K-C (2017c) pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol Biosyst 13:1722–1727

    Article  CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou K-C (2017d) pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene 628:315–321

    Article  CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou K-C (2018) pLoc-mEuk: predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110:50–58

    Article  CAS  PubMed  Google Scholar 

  • Chou KC (2001a) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Bioinform 43:246–255

    Article  CAS  Google Scholar 

  • Chou K-C (2001b) Prediction of signal peptides using scaled window. Peptides 22:1973–1979

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem 11:218–234

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C (2017) An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem 17:2337–2358

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C, Shen H-B (2007a) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 6:1728–1734

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C, Shen H-B (2007b) Recent progress in protein subcellular location prediction. Anal Biochem 370:1–16

    Article  CAS  PubMed  Google Scholar 

  • Chou K-C, Shen H-B (2007c) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Commun 357:633–640

    Article  CAS  PubMed  Google Scholar 

  • Dong C, Yuan Y-Z, Zhang F-Z, Hua H-L, Ye Y-N, Labena AA, Lin H, Chen W, Guo F-B (2016) Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots. Mol BioSyst 12:2893–2900

    Article  CAS  PubMed  Google Scholar 

  • Eddy SR (1996) Hidden markov models. Curr Opin Struct Biol 6:361–365

    Article  CAS  PubMed  Google Scholar 

  • Ehsan A, Mahmood K, Khan YD, Khan SA, Chou K-C (2018) A novel modeling in mathematical biology for classification of signal peptides. Sci Rep 8:1039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Feng P, Ding H, Yang H, Chen W, Lin H, Chou K-C (2017) iRNA-PseColl: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol Ther Nucleic Acids 7:155–163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Feng P, Yang H, Ding H, Lin H, Chen W, Chou K-C (2018) iDNA6mA-PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics. https://doi.org/10.1016/j.ygeno.2018.01.005

    Article  PubMed  Google Scholar 

  • Field Y, Kaplan N, Fondufe-Mittendorf Y, Moore IK, Sharon E, Lubling Y, Widom J, Segal E (2008) Distinct modes of regulation by chromatin encoded through nucleosome positioning signals. PLoS Comput Biol 4:e1000216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gabdank I, Barash D, Trifonov EN (2010) Single-base resolution nucleosome mapping on DNA sequences. J Biomol Struct Dyn 28:107–121

    Article  CAS  PubMed  Google Scholar 

  • Goñi JR, Fenollosa C, Pérez A, Torrents D, Orozco M (2008) DNAlive: a tool for the physical analysis of DNA at the genomic scale. Bioinformatics 24:1731–1732

    Article  CAS  PubMed  Google Scholar 

  • Guo S-H, Deng E-Z, Xu L-Q, Ding H, Lin H, Chen W, Chou K-C (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30(11):1522–1529

    Article  CAS  PubMed  Google Scholar 

  • Hayat M, Khan A (2012) Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou’s PseAAC. Protein Pept Lett 19:411–421

    Article  CAS  PubMed  Google Scholar 

  • Hayat M, Tahir M (2015) PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine. Mol BioSyst 11:2255–2262

    Article  CAS  PubMed  Google Scholar 

  • Ioshikhes I, Bolshoy A, Derenshteyn K, Borodovsky M, Trifonov EN (1996) Nucleosome DNA sequence pattern revealed by multiple alignment of experimentally mapped sequences. J Mol Biol 262:129–139

    Article  CAS  PubMed  Google Scholar 

  • Isami S, Sakamoto N, Nishimori H, Awazu A (2015) Simple elastic network models for exhaustive analysis of long double-stranded DNA dynamics with sequence geometry dependence. PLoS One 10:e0143760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jia J, Liu Z, Xiao X, Liu B, Chou K-C (2016) pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 394:223–230

    Article  CAS  PubMed  Google Scholar 

  • Kabir M, Hayat M (2016) iRSpot-GAEnsC: identifying recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genom 291:285–296

    Article  CAS  Google Scholar 

  • Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ, Tillo D, Field Y, LeProust EM, Hughes TR, Lieb JD, Widom J (2009) The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458:362–366

    Article  CAS  Google Scholar 

  • Levitsky VG (2004) RECON: a program for prediction of nucleosome formation potential. Nucleic Acids Res 32:W346–W349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li W-C, Deng E-Z, Ding H, Chen W, Lin H (2015) iORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide composition. Chemom Intell Lab Syst 141:100–106

    Article  CAS  Google Scholar 

  • Li D, Luo L, Zhang W, Liu F, Luo F (2016) A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinform 17:329

    Article  Google Scholar 

  • Lin H, Deng E-Z, Ding H, Chen W, Chou K-C (2014) iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42:12961–12972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu B, Zhang D, Xu R, Xu J, Wang X, Chen Q, Dong Q, Chou K-C (2014a) Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30:472–479

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, Chou K-C (2014b) iDNA-Prot| dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One 9:e106691

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu B, Liu F, Fang L, Wang X, Chou K-C (2015a) repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31:1307–1309

    Article  PubMed  Google Scholar 

  • Liu Z, Xiao X, Qiu W-R, Chou K-C (2015c) iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474:69–77

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Fang L, Liu F, Wang X, Chen J, Chou K-C (2015d) Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One 10:e0121501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu G-H, Shen H-B, Yu D-J (2016a) Prediction of protein–protein interaction sites with machine-learning-based data-cleaning and post-filtering procedures. J Membr Biol 249:141–153

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Long R, Chou K-C (2016b) iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics 32(16):2411–2418

    Article  PubMed  Google Scholar 

  • Liu B, Wang S, Long R, Chou K-C (2016c) iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics 33:35–41

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Yang F, Huang D-S, Chou K-C (2017a) iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 34:33–40

    Article  CAS  Google Scholar 

  • Liu B, Yang F, Chou K-C (2017b) 2L-piRNA: a two-layer ensemble classifier for identifying Piwi-interacting RNAs and their function. Mol Ther Nucleic Acids 7:267–277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu B, Wu H, Zhang D, Wang X, Chou K-C (2017c) Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget 8:13338

    PubMed  PubMed Central  Google Scholar 

  • Liu B, Li K, Huang D-S, Chou K-C (2018) iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty458

    Article  PubMed  PubMed Central  Google Scholar 

  • Luo L, Li D, Zhang W, Tu S, Zhu X, Tian G (2016) Accurate prediction of transposon-derived piRNAs by integrating various sequential and physicochemical features. PLoS One 11:e0153268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Manavalan B, Shin TH, Lee G (2018) PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine. Front Microbiol 9:476

    Article  PubMed  PubMed Central  Google Scholar 

  • Mavrich TN, Jiang C, Ioshikhes IP, Li X, Venters BJ, Zanton SJ, Tomsho LP, Qi J, Glaser RL, Schuster SC (2008a) Nucleosome organization in the Drosophila genome. Nature 453:358–362

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, Qi J, Schuster SC, Albert I, Pugh BF (2008b) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome Res 18:1073–1083

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, Qi J, Schuster SC, Albert I, Pugh BF (2008c) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome Res

  • Nikolaou C, Althammer S, Beato M, Guigó R (2010) Structural constraints revealed in consistent nucleosome positions in the genome of S. cerevisiae. Epigenetics Chromatin 3:20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peckham HE, Thurman RE, Fu Y, Stamatoyannopoulos JA, Noble WS, Struhl K, Weng Z (2007) Nucleosome positioning signals in genomic DNA. Genome Res 17:1170–1177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qiu W-R, Xiao X, Chou K-C (2014) iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 15:1746–1766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Satchwell SC, Drew HR, Travers AA (1986) Sequence periodicities in chicken nucleosome core DNA. J Mol Biol 191:659–675

    Article  CAS  PubMed  Google Scholar 

  • Schwartz S, Meshorer E, Ast G (2009) Chromatin organization marks exon–intron structure. Nat Struct Mol Biol 16:990

    Article  CAS  PubMed  Google Scholar 

  • Segal E, Fondufe-Mittendorf Y, Chen L, Thåström A, Field Y, Moore IK, Wang J-PZ, Widom J (2006) A genomic code for nucleosome positioning. Nature 442:772–778

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stolz RC, Bishop TC (2010) ICM Web: the interactive chromatin modeling web server. Nucleic Acids Res 38:W254–W261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tahir M, Hayat M (2016) iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC. Mol BioSyst 12:2587–2593

    Article  CAS  PubMed  Google Scholar 

  • Thoma F, Koller T, Klug A (1979) Involvement of histone H1 in the organization of the nucleosome and of the salt-dependent superstructures of chromatin. J Cell Biol 83:403–427

    Article  CAS  PubMed  Google Scholar 

  • Tian K, Yang X, Kong Q, Yin C, He RL, Yau SS-T (2015) Two dimensional Yau-hausdorff distance with applications on comparison of DNA and protein sequences. PLoS One 10:e0136577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tolstorukov MY, Choudhary V, Olson WK, Zhurkin VB, Park PJ (2008) nuScore: a web-interface for nucleosome positioning predictions. Bioinformatics 24:1456–1458

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xi L, Fondufe-Mittendorf Y, Xia L, Flatow J, Widom J, Wang J-P (2010) Predicting nucleosome positioning using a duration Hidden Markov Model. BMC Bioinform 11:1

    Article  CAS  Google Scholar 

  • Xiang S, Liu K, Yan Z, Zhang Y, Sun Z (2016) RNAMethPre: a web server for the prediction and query of mRNA m 6 A sites. PLoS One 11:e0162707

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xiao X, Wang P, Lin W-Z, Jia J-H, Chou K-C (2013) iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436:168–177

    Article  CAS  PubMed  Google Scholar 

  • Xiao X, Cheng X, Su S, Mao Q, Chou K-C (2017) pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat Sci 9:330

    Google Scholar 

  • Xiao X, Cheng X, Chen G, Mao Q, Chou K-C (2018) pLoc-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics. https://doi.org/10.1016/j.ygeno.2018.05.017

    Article  PubMed  Google Scholar 

  • Xu Y, Shao X-J, Wu L-Y, Deng N-Y, Chou K-C (2013a) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1:e171

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Ding J, Wu L-Y, Chou K-C (2013b) iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One 8:e55844

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Wen X, Wen L-S, Wu L-Y, Deng N-Y, Chou K-C (2014) iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One 9:e105018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yasuda T, Sugasawa K, Shimizu Y, Iwai S, Shiomi T, Hanaoka F (2005) Nucleosomal structure of undamaged DNA regions suppresses the non-specific DNA binding of the XPC complex. DNA Repair 4:389–395

    Article  CAS  PubMed  Google Scholar 

  • YongE F, GaoShan K (2015) Identify beta-hairpin motifs with quadratic discriminant algorithm based on the chemical shifts. PLoS One 10:e0139280

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yuan G-C, Liu JS (2008) Genomic sequence is highly predictive of local nucleosome depletion. PLoS Comput Biol 4:e13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yuan G-C, Liu Y-J, Dion MF, Slack MD, Wu LF, Altschuler SJ, Rando OJ (2005) Genome-scale identification of nucleosome positions in S. cerevisiae. Science 309:626–630

    Article  CAS  Google Scholar 

  • Zhang W, Niu Y, Xiong Y, Zhao M, Yu R, Liu J (2012) Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PLoS One 7:e43575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang W, Liu F, Luo L, Zhang J (2015a) Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinform 16:365

    Article  Google Scholar 

  • Zhang W, Niu Y, Zou H, Luo L, Liu Q, Wu W (2015b) Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS one 10:e0128194

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016a) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–987

    Article  Google Scholar 

  • Zhang C-J, Tang H, Li W-C, Lin H, Chen W, Chou K-C (2016b) iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 7:69783

    PubMed  PubMed Central  Google Scholar 

  • Zhang W, Shi J, Tang G, Wu W, Yue X, Li D (2017) Predicting small RNAs in bacteria via sequence learning ensemble method. In: Bioinformatics and biomedicine (BIBM), 2017 IEEE international conference on, IEEE, pp 643–647

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan

    Muhammad Tahir, Maqsood Hayat & Sher Afzal Khan

Authors
  1. Muhammad Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maqsood Hayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sher Afzal Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maqsood Hayat.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Additional information

Communicated by S. Hohmann.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tahir, M., Hayat, M. & Khan, S.A. iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition. Mol Genet Genomics 294, 199–210 (2019). https://doi.org/10.1007/s00438-018-1498-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00438-018-1498-2

Keywords

Search

Navigation