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A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule

Abstract

Piwi interacting RNA (piRNA) molecules belong to a largest class of small non coding RNA molecules which are originally discovered in animal germline cells and also occur across a variety of human somatic cells. The piRNA molecules play a significant role in many gene functions such as protecting genomic integrity, gene expression regulation and restricting the functions of transposable elements. The identification of piRNA molecules and their function types are significant for cancer cells diagnosis, drug developments and genes stability. A number of traditional machine learning methods have been proposed for identification of piRNAs and their functions. However, these methods are required a considerable amounts of human engineering and expertise to design an accurate identification model. Hence, this paper proposes a two level computational model based on deep neural network (DNN) that automatically extract informative features from RNA sequences using standard learning methods. Moreover, the proposed model employs di-nucleotide auto covariance (DAC) method along with six physiochemical properties to construct a feature vector. The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests. Firstly, the performance of the proposed model is compared with commonly used classifier algorithms using benchmark dataset. Secondly, its performance is compared with the existing state-of- the-art computational models. The experimental results show that the proposed model performed better than the existing predictors with accuracy level 91.81% and 84.52% in the first level and in the second level respectively. The source code along with dataset of the proposed model is freely available at https://github.com/salman-khan-mrd/2L-piRNADNN.

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References

  1. Acharya UR, Lih S, Hagiwara Y et al (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278. https://doi.org/10.1016/j.compbiomed.2017.09.017

    Article  PubMed  PubMed Central  Google Scholar 

  2. Alipanahi B, Delong A, Weirauch MT, Frey BJ (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. https://doi.org/10.1038/nbt.3300

    Article  PubMed  Google Scholar 

  3. Althaus IW, Chou JJ, Gonzales AJ et al (1993a) Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem 268(9):6119–6124

    CAS  PubMed  Google Scholar 

  4. Althaus IW, Chou JJ, Gonzales AJ et al (1993b) Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 32:6554–6648

    Article  Google Scholar 

  5. Althaus IW, Gonzales AJ, Chou JJ et al (1993c) The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem 268(20):14875–14880

    CAS  PubMed  Google Scholar 

  6. Althaus IW, Chou JJ, Gonzales AJ et al (1994a) Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia. https://doi.org/10.1007/bf01992044

    Article  PubMed  Google Scholar 

  7. Althaus IW, Chou JJ, Gonzales AJ et al (1994b) Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem Pharmacol. https://doi.org/10.1016/0006-2952(94)90077-9

    Article  PubMed  Google Scholar 

  8. Althaus IW, Chou KC, Lemay RJ et al (1996) The benzylthio-pyrimidine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol. https://doi.org/10.1016/0006-2952(95)02390-9

    Article  PubMed  Google Scholar 

  9. Andraos J (2008) Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws—new methods based on directed graphs. Can J Chem. https://doi.org/10.1139/v08-020

    Article  Google Scholar 

  10. Aravin A, Gaidatzis D, Pfeffer S et al (2006) A novel class of small RNAs bind to MILI protein in mouse testes. Nature 442:203–207. https://doi.org/10.1038/nature04916

    CAS  Article  PubMed  Google Scholar 

  11. Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry. W H Free, New York, pp 320–323

    Google Scholar 

  12. Bordes A, Chopra S, Weston J (2014) Question answering with subgraph embeddings. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 615–620

  13. Bu D, Yu K, Sun S et al (2012) NONCODE v30: integrative annotation of long noncoding RNAs. Nucleic Acids Res. https://doi.org/10.1093/nar/gkr1175

    Article  PubMed  PubMed Central  Google Scholar 

  14. Carter RE, Forsen S (1981) A new graphical method for driving rate equations for complicated mechanisms. Chem Scr 18:82–86

    Google Scholar 

  15. Chen J, Liu H, Yang J, Chou KC (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33:423–428. https://doi.org/10.1007/s00726-006-0485-9

    CAS  Article  PubMed  Google Scholar 

  16. Chen W, Feng PM, Lin H, Chou KC (2013) IRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41:1–9. https://doi.org/10.1093/nar/gks1450

    CAS  Article  Google Scholar 

  17. Chen W, Lei TY, Jin DC et al (2014) PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456:53–60. https://doi.org/10.1016/j.ab.2014.04.001

    CAS  Article  PubMed  Google Scholar 

  18. Chen W, Tang H, Ye J et al (2016) iRNA-PseU: identifying RNA pseudouridine sites. Mol Ther Nucleic Acids. https://doi.org/10.1038/mtna.2016.37

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen Y, Li T, Song R et al (2018) Support vector machine classifier for accurate identification of piRNA. Appl Sci. https://doi.org/10.3390/app8112204

    Article  PubMed  Google Scholar 

  20. Cheng J, Deng H, Xiao B et al (2012) PiR-823, a novel non-coding small RNA, demonstrates in vitro and in vivo tumor suppressive activity in human gastric cancer cells. Cancer Lett. https://doi.org/10.1016/j.canlet.2011.10.004

    Article  PubMed  Google Scholar 

  21. Cheng D, Zhang S, Deng Z et al (2014) kNN algorithm with data-driven k value. In: Luo X, Yu JX, Li Z (eds) Advanced data mining and applications. Springer International Publishing, Cham, pp 499–512

    Chapter  Google Scholar 

  22. Cheng X, Lin WZ, Xiao X, Chou KC (2019) PLoc-bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty628

    Article  PubMed  PubMed Central  Google Scholar 

  23. Chou K-C (1989) Graphic rules in steady and non-steady state enzyme kinetics. J Biol Chem 264(20):12074–12079

    CAS  PubMed  Google Scholar 

  24. Chou K-C (1990) Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys Chem 35(1):1–24

    CAS  PubMed  Google Scholar 

  25. Chou K-C (2001) Using subsite coupling to predict signal peptides. Protein Eng Des Sel 14:75–79. https://doi.org/10.1093/protein/14.2.75

    CAS  Article  Google Scholar 

  26. Chou K-C (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19. https://doi.org/10.1093/bioinformatics/bth466

    CAS  Article  PubMed  Google Scholar 

  27. Chou K-C (2010) Graphic rule for drug metabolism systems. Curr Drug Metab 11(4):369–378

    CAS  Article  Google Scholar 

  28. Chou K-C (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273:236–247

    CAS  Article  Google Scholar 

  29. Chou K-C (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem (Los Angeles). https://doi.org/10.2174/1573406411666141229162834

    Article  Google Scholar 

  30. Chou K-C (2017) An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem. https://doi.org/10.2174/1568026617666170414145508

    Article  PubMed  Google Scholar 

  31. Chou K-C (2019) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr Med Chem. https://doi.org/10.2174/0929867326666190507082559

    Article  PubMed  Google Scholar 

  32. Chou K-C, Forsen S (1981) The biological functions of low-frequency phonons: 2 cooperative effects. Chem Scr 1981:126–132

    Google Scholar 

  33. Chou K-C, Forsén S (1980a) Graphical rules for enzyme-catalysed rate laws. Biochem J. https://doi.org/10.1042/bj1870829

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chou K-C, Forsén S (1980b) Diffusion-controlled effects in reversible enzymatic fast reaction systems—critical spherical shell and proximity rate constant. Biophys Chem. https://doi.org/10.1016/0301-4622(80)80002-0

    Article  PubMed  Google Scholar 

  35. Chou K-C, Shen H-B (2009a) Review: recent advances in developing web-servers for predicting protein attributes. Nat Sci 01:63–92. https://doi.org/10.4236/ns.2009.12011

    CAS  Article  Google Scholar 

  36. Chou K-C, Shen H-B (2009b) Review : recent advances in developing web-servers for predicting protein attributes. Nat Sci. https://doi.org/10.4236/ns.2009.12011

    Article  Google Scholar 

  37. Chou K-C, Zhang CT (1995) Prediction of protein structural classes. Crit Rev Biochem Mol Biol 30:275–349. https://doi.org/10.3109/10409239509083488

    CAS  Article  PubMed  Google Scholar 

  38. Chou K-C, Forsen S, Zhou G-Q (1980a) Three schematic rules for deriving apparent rate constants. Chem Scr 16:109–113

    Google Scholar 

  39. Chou K-C, Li TT, Forsén S (1980b) The critical spherical shell in enzymatic fast reaction systems. Biophys Chem. https://doi.org/10.1016/0301-4622(80)80003-2

    Article  PubMed  Google Scholar 

  40. Chou K-C, Kézdy FJ, Reusser F (1994) Kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem 221(2):217–230

    CAS  Article  Google Scholar 

  41. Chou K-C, Lin W-Z, Xiao X (2011) Wenxiang: a web-server for drawing wenxiang diagrams. Nat Sci. 1:1. https://doi.org/10.4236/ns.2011.310111

    CAS  Article  Google Scholar 

  42. Claverie JM (2005) Fewer genes, more noncoding RNA. Science 309:1529–1530

    CAS  Article  Google Scholar 

  43. Cox DN, Chao A, Baker J et al (1998) A novel class of evolutionarily conserved genes defined by piwi are essential for stem cell self-renewal. Genes Dev 12:3715–3727. https://doi.org/10.1101/gad.12.23.3715

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Dehzangi A, Heffernan R, Sharma A et al (2015) Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou’s general PseAAC. J Theor Biol 364:284–294. https://doi.org/10.1016/j.jtbi.2014.09.029

    CAS  Article  PubMed  Google Scholar 

  45. Dong Q, Zhou S, Guan J (2009) A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics 25:2655–2662. https://doi.org/10.1093/bioinformatics/btp500

    CAS  Article  PubMed  Google Scholar 

  46. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. https://doi.org/10.1186/1471-2105-8-4

    Article  Google Scholar 

  47. Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35:1915–1929. https://doi.org/10.1109/TPAMI.2012.231

    Article  PubMed  Google Scholar 

  48. Fawagreh K, Gaber MM, Elyan E (2014) Random forests: from early developments to recent advancements. Syst Sci Control Eng 2:602–609. https://doi.org/10.1080/21642583.2014.956265

    Article  Google Scholar 

  49. Fu L, Niu B, Zhu Z et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. PMLR 9:249–256

    Google Scholar 

  51. Grivna ST, Beyret E, Wang Z, Lin H (2006) A novel class of small RNAs in mouse spermatogenic cells. Genes Dev 20:1709–1714. https://doi.org/10.1101/gad.1434406

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Guo Y, Li M, Lu M et al (2006) Predicting G-protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform. Proteins Struct Funct Genet 65:55–60. https://doi.org/10.1002/prot.21097

    CAS  Article  PubMed  Google Scholar 

  53. Guo Y, Yu L, Wen Z, Li M (2008) Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res 36:3025–3030. https://doi.org/10.1093/nar/gkn159

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Gupta D (2017) Fundamentals of deep learning—activation functions and when to use them. https://www.analyticsvidhya.com/blog/2017/10/fundamentals-deep-learning-activation-functions-when-to-use-them/. Accessed 25 Sep 2018

  55. Harrington S (2017) Gradient descent: high learning rates & divergence

  56. Hashim A, Rizzo F, Marchese G et al (2014) RNA sequencing identifies specific PIWI-interacting small non-coding RNA expression patterns in breast cancer. Oncotarget 5:9901–9910. https://doi.org/10.18632/oncotarget.2476

    Article  PubMed  PubMed Central  Google Scholar 

  57. Helmstaedter M, Briggman KL, Turaga SC et al (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500:168

    CAS  Article  Google Scholar 

  58. Hinton G, Deng L, Yu D et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97. https://doi.org/10.1109/MSP.2012.2205597

    Article  Google Scholar 

  59. Houwing S, Kamminga LM, Berezikov E et al (2007) A role for Piwi and piRNAs in germ cell maintenance and transposon silencing in zebrafish. Cell 129:69–82. https://doi.org/10.1016/j.cell.2007.03.026

    CAS  Article  PubMed  Google Scholar 

  60. Huang Y, Liu N, Wang JP et al (2012) Regulatory long non-coding RNA and its functions. J Physiol Biochem 68:611–618

    CAS  Article  Google Scholar 

  61. Jeong JC, Lin X, Chen X-W (2011) On position-specific scoring matrix for protein function prediction. IEEE/ACM Trans Comput Biol Bioinform 8:308–315. https://doi.org/10.1109/TCBB.2010.93

    Article  PubMed  Google Scholar 

  62. Jia J, Liu Z, Xiao X et al (2016a) IPPBS-Opt: A sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules. https://doi.org/10.3390/molecules21010095

    Article  PubMed  PubMed Central  Google Scholar 

  63. Jia J, Liu Z, Xiao X et al (2016b) ISuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem 497:48–56. https://doi.org/10.1016/j.ab.2015.12.009

    CAS  Article  PubMed  Google Scholar 

  64. Jiang SP, Liu WM, Fee CH (1979) Graph theory of enzyme kinetics: 1. Steady-state reaction system. Sci Sin 22:341–358

    Google Scholar 

  65. Ju Z, Cao JZ, Gu H (2016) Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. J Theor Biol 397:145–150. https://doi.org/10.1016/j.jtbi.2016.02.020

    CAS  Article  PubMed  Google Scholar 

  66. Klattenhoff C, Theurkauf W (2007) Biogenesis and germline functions of piRNAs. Development 135:3–9. https://doi.org/10.1242/dev.006486

    CAS  Article  PubMed  Google Scholar 

  67. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1. Curran Associates Inc., USA, pp 1097–1105

  68. Kuo-Chen C, Forsen S (2006) Graphical rules of steady-state reaction systems. Can J Chem. https://doi.org/10.1139/v81-107

    Article  Google Scholar 

  69. Lau NC, Seto AG, Kim J et al (2006) Characterization of the piRNA complex from rat testes. Science 313:363–367. https://doi.org/10.1126/science.1130164

    CAS  Article  PubMed  Google Scholar 

  70. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    CAS  Article  PubMed  Google Scholar 

  71. Leung MKK, Xiong HY, Lee LJ, Frey BJ (2014) Deep learning of the tissue-regulated splicing code. Bioinformatics 30:121–129. https://doi.org/10.1093/bioinformatics/btu277

    CAS  Article  Google Scholar 

  72. Li TT, Chou KC (1980) The flow of substrate molecules in fast enzyme catalyzed reaction systems. Chem Scr 16:192–196

    CAS  Google Scholar 

  73. Li D, Luo L, Zhang W et al (2016) A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinform. https://doi.org/10.1186/s12859-016-1206-3

    Article  Google Scholar 

  74. Lin H, Deng EZ, Ding H et al (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. https://doi.org/10.1093/nar/gku1019

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. Liu B (2017) BioSeq-Analysis: a platform for DNA, RNA, and protein sequence analysis based on machine learning approaches. Brief Bioinform. https://doi.org/10.1093/bib/bbx165

    Article  PubMed  PubMed Central  Google Scholar 

  76. Liu Z, Xiao X, Qiu W-R, Chou K-C (2015) iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474:69–77. https://doi.org/10.1016/j.ab.2014.12.009

    CAS  Article  PubMed  Google Scholar 

  77. Liu B, Liu F, Fang L et al (2016) repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genom 291:473–481. https://doi.org/10.1007/s00438-015-1078-7

    CAS  Article  Google Scholar 

  78. Liu B, Yang F, Chou KC (2017) 2L-piRNA: a two-layer ensemble classifier for identifying Piwi-interacting RNAs and their function. Mol Ther Nucleic Acids 7:267–277. https://doi.org/10.1016/j.omtn.2017.04.008

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  79. Luo L, Li D, Zhang W et al (2016) Accurate prediction of transposon-derived piRNAs by integrating various sequential and physicochemical features. PLoS ONE. https://doi.org/10.1371/journal.pone.0153268

    Article  PubMed  PubMed Central  Google Scholar 

  80. Ma J, Sheridan RP, Liaw A et al (2015) Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 55:263–274. https://doi.org/10.1021/ci500747n

    CAS  Article  PubMed  Google Scholar 

  81. Mattick JS (2005) The functional genomics of noncoding RNA. Sci (New York, NY) 309:1527–1528. https://doi.org/10.1126/science.1117806

    CAS  Article  Google Scholar 

  82. Meenakshisundaram K, Carmen L, Michela B et al (2009) Existence of snoRNA, microRNA, piRNA characteristics in a novel non-coding RNA: x-ncRNA and its biological implication in Homo sapiens. J Bioinform Seq Anal 1:31–40

    CAS  Google Scholar 

  83. Mei Y, Clark D, Mao L (2013) Novel dimensions of piRNAs in cancer. Cancer Lett 336:46–52

    CAS  Article  Google Scholar 

  84. Miao JH, Miao KH (2018) Cardiotocographic diagnosis of fetal health based on multiclass morphologic pattern predictions using deep learning classification. Int J Adv Comput Sci Appl 9:1–11

    CAS  Google Scholar 

  85. Mikolov T, Kombrink S, Burget L, et al (2011) Extensions of recurrent neural network language model. In: 2011 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 5528–5531

  86. Min S, Lee B, Yoon S (2016) Deep learning in bioinformatics. Brief Bioinform. https://doi.org/10.1093/bib/bbw068

    Article  PubMed  Google Scholar 

  87. Mondal S, Pai PP (2014) Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol 356:30–35. https://doi.org/10.1016/j.jtbi.2014.04.006

    CAS  Article  PubMed  Google Scholar 

  88. Moyano M, Stefani G (2015) piRNA involvement in genome stability and human cancer. J Hematol Oncol 8:38. https://doi.org/10.1186/s13045-015-0133-5

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. Nielsen M (2017) Neural networks and deep learning

  90. Noi PT, Kappas M (2018) Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors (Switzerland). https://doi.org/10.3390/s18010018

    Article  Google Scholar 

  91. Ravi D, Wong C, Deligianni F et al (2017) Deep learning for health informatics. IEEE J Biomed Heal Informatics 21:4–21. https://doi.org/10.1109/JBHI.2016.2636665

    Article  Google Scholar 

  92. Sabooh MF, Iqbal N, Khan M et al (2018) Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J Theor Biol 452:1–9. https://doi.org/10.1016/j.jtbi.2018.04.037

    CAS  Article  PubMed  Google Scholar 

  93. Sainath TN, Mohamed AR, Kingsbury B, Ramabhadran B (2013) Deep convolutional neural networks for LVCSR. In: 2013 IEEE international conference on acoustics, speech and signal processing. pp 8614–8618

  94. Shen H-B, Song J-N, Chou K-C (2009) Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J Biomed Sci Eng. https://doi.org/10.4236/jbise.2009.23024

    Article  Google Scholar 

  95. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. https://doi.org/10.1214/12-AOS1000

    Article  Google Scholar 

  96. Tang H, Zou P, Zhang C et al (2016) Identification of apolipoprotein using feature selection technique. Sci Rep 6:1–6. https://doi.org/10.1038/srep30441

    CAS  Article  Google Scholar 

  97. Tompson J, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. ArXiv e-prints

  98. Tripathi R, Patel S, Kumari V et al (2016) DeepLNC, a long non-coding RNA prediction tool using deep neural network. Netw Model Anal Heal Inform Bioinform 5:21. https://doi.org/10.1007/s13721-016-0129-2

    Article  Google Scholar 

  99. Wang K, Liang C, Liu J et al (2014) Prediction of piRNAs using transposon interaction and a support vector machine. BMC Bioinform. https://doi.org/10.1186/s12859-014-0419-6

    Article  Google Scholar 

  100. Wen Z, Li M, Li Y et al (2007) Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition. Amino Acids 32:277–283. https://doi.org/10.1007/s00726-006-0341-y

    CAS  Article  PubMed  Google Scholar 

  101. Wikipedia 5-step rules. https://en.wikipedia.org/wiki/5-step_rules. Accessed 25 Jun 2019

  102. Wold S, Jonsson J, Sjörström M et al (1993) DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 277:239–253. https://doi.org/10.1016/0003-2670(93)80437-P

    CAS  Article  Google Scholar 

  103. Xiao X, Cheng X, Chen G et al (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 

  104. Xie C, Yuan J, Li H et al (2014) NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt1222

    Article  PubMed  PubMed Central  Google Scholar 

  105. Xu Y, Ding J, Wu LY, Chou KC (2013) iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS ONE. https://doi.org/10.1371/journal.pone.0055844

    Article  PubMed  PubMed Central  Google Scholar 

  106. Xu ZC, Wang P, Qiu WR, Xiao X (2017) ISS-PC: identifying splicing sites via physical-chemical properties using deep sparse auto-encoder. Sci Rep 7:1–12. https://doi.org/10.1038/s41598-017-08523-8

    CAS  Article  Google Scholar 

  107. Yue S, Li P, Hao P (2003) SVM classification: its contents and challenges. Appl Math J Chinese Univ 18:332–342. https://doi.org/10.1007/s11766-003-0059-5

    Article  Google Scholar 

  108. Zhang Y, Wang X, Kang L (2011) A k-mer scheme to predict piRNAs and characterize locust piRNAs. Bioinformatics 27:771–776. https://doi.org/10.1093/bioinformatics/btr016

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  109. Zhang P, Si X, Skogerbø G et al (2014) PiRBase: a web resource assisting piRNA functional study. Database. https://doi.org/10.1093/database/bau110

    Article  PubMed  PubMed Central  Google Scholar 

  110. Zhou GP (2011) The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol. https://doi.org/10.1016/j.jtbi.2011.06.006

    Article  PubMed  PubMed Central  Google Scholar 

  111. Zhou GP, Deng MH (1984) An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J 222(1):169–176

    CAS  Article  Google Scholar 

  112. Zhu Z, Albadawy E, Saha A et al (2019) Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med 109:85–90. https://doi.org/10.1016/j.compbiomed.2019.04.018

    Article  PubMed  Google Scholar 

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Khan, S., Khan, M., Iqbal, N. et al. A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. Int J Pept Res Ther 26, 795–809 (2020). https://doi.org/10.1007/s10989-019-09887-3

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Keywords

  • piRNAs (Piwi-interacting RNAs)
  • Deep neural network
  • Dinucleotide based auto covariance
  • Machine learning