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A Review of Artificial Neural Networks for the Prediction of Essential Proteins

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Networks in Systems Biology

Part of the book series: Computational Biology ((COBO,volume 32))

Abstract

Identifying essential proteins is vital to understanding the minimum requirements for maintaining life. The correct identification of essential proteins contributes to guide the diagnosis of diseases and to identify new drug targets. The continuous advances of experimental methods contribute to generate and accumulate gene essentiality data that facilitates computational methods. Machine learning methods focused on predicting essential proteins have gained much traction in recent years. Among them, we can highlight artificial neural networks. Most of these methods make use of sequence and network-based features. In this chapter, we initially present a background related to artificial neural networks, which encompasses different neural network architectures. Then, we review the research papers that used artificial neural networks as a machine learning method for predicting essential proteins.

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References

  1. Acencio ML, Lemke N (2009) Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information. BMC Bioinform 10:290. https://doi.org/10.1186/1471-2105-10-290

    Article  Google Scholar 

  2. Ashtiani M, Salehzadeh-Yazdi A, Razaghi-Moghadam Z, Hennig H, Wolkenhauer O, Mirzaie M, Jafari M (2018) A systematic survey of centrality measures for protein-protein interaction networks. BMC Syst Biol 12(1). https://doi.org/10.1186/s12918-018-0598-2

  3. Bakar S, Taheri J, Zomaya A (2011) Identifying hub proteins and their essentiality from protein-protein interaction network. In: Proceedings—2011 11th IEEE international conference on bioinformatics and bioengineering, BIBE 2011, pp 266–269. https://doi.org/10.1109/BIBE.2011.67

  4. Bakar S, Taheri J, Zomaya A (2014) Characterization of essential proteins based on network topology in proteins interaction networks. AIP Conf Proc 1602:36–42. https://doi.org/10.1063/1.4882463

    Article  Google Scholar 

  5. Burkov A (2019) The hundred-page machine learning book. Andriy Burkov, http://themlbook.com/wiki/doku.php

  6. Campos T, Korhonen P, Gasser R, Young N (2019) An evaluation of machine learning approaches for the prediction of essential genes in eukaryotes using protein sequence-derived features. Comput Struct Biotechnol J 17:785–796. https://doi.org/10.1016/j.csbj.2019.05.008

    Article  Google Scholar 

  7. Chen WH, Minguez P, Lercher MJ, Bork P (2011) Ogee: an online gene essentiality database. NuclC Acids Res 40(D1):D901–D906

    Article  Google Scholar 

  8. Cullen LM, Arndt GM (2005) Genome-wide screening for gene function using RNAi in mammalian cells. Immunol Cell Biol 83(3):217–223. https://doi.org/10.1111/j.1440-1711.2005.01332.x

    Article  Google Scholar 

  9. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control, Signals, Syst (MCSS) 2(4):303–314. https://doi.org/10.1007/BF02551274

  10. Dai W, Chang Q, Peng W, Zhong J, Li Y (2020) Network embedding the protein–protein interaction network for human essential genes identification. Genes 11(2). https://doi.org/10.3390/genes11020153

  11. Deng J, Deng L, Su S, Zhang M, Lin X, Wei L, Minai AA, Hassett DJ, Lu LJ (2011) Investigating the predictability of essential genes across distantly related organisms using an integrative approach. Nucl Acids Res 39(3):795–807. https://doi.org/10.1093/nar/gkq784

    Article  Google Scholar 

  12. Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28, Curran associates, Inc., pp 2224–2232. http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf

  13. Evers B, Jastrzebski K, Heijmans J, Grernrum W, Beijersbergen R, Bernards R (2016) CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat Biotechnol 34(6):631–633. https://doi.org/10.1038/nbt.3536

    Article  Google Scholar 

  14. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. In: IET conference proceedings, vol 5, pp 850–855

    Google Scholar 

  15. Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. Int Conf Mach Learn 3:2029–2042

    Google Scholar 

  16. Gong X, Fan S, Bilderbeck A, Li M, Pang H, Tao S (2008) Comparative analysis of essential genes and nonessential genes in Escherichia coli K12. Mol Genet Genomics 279(1):87–94. https://doi.org/10.1007/s00438-007-0298-x

    Article  Google Scholar 

  17. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org

  18. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining—KDD ’16. ACM Press, San Francisco, California, USA, pp 855–864. https://doi.org/10.1145/2939672.2939754

  19. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  20. Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with protein-protein interactions. Genome Res 12(1):37–46. https://doi.org/10.1101/gr.205602

    Article  Google Scholar 

  21. King Jordan I, Rogozin IB, Wolf YI, Koonin EV (2002) Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res 12(6):962–968. https://doi.org/10.1101/gr.87702. Article published online before print in May 2002

  22. Kondrashov F, Ogurtsov A, Kondrashov A (2004) Bioinformatical assay of human gene morbidity. Nucl Acids Res 32(5):1731–1737. https://doi.org/10.1093/nar/gkh330

    Article  Google Scholar 

  23. 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 (NeurIPS). Curran Associates Inc., pp 1097–1105

    Google Scholar 

  24. LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, USA, pp 1–14

    Google Scholar 

  25. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  26. Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N (2018) A survey on addressing high-class imbalance in big data. J Big Data 5(1):42. https://doi.org/10.1186/s40537-018-0151-6

    Article  Google Scholar 

  27. Lei X, Yang X (2018) A new method for predicting essential proteins based on participation degree in protein complex and subgraph density. PLOS ONE 13(6):e0198,998. https://doi.org/10.1371/journal.pone.0198998

  28. Lu Y, Deng J, Carson M, Lu H, Lu L (2014) Computational methods for the prediction of microbial essential genes. Curr Bioinform 9(2):89–101. https://doi.org/10.2174/1574893608999140109113434

    Article  Google Scholar 

  29. Mobegi F, Zomer A, de Jonge M, van Hijum S (2017) Advances and perspectives in computational prediction of microbial gene essentiality. Brief Funct Genomics 16(2):70–79. https://doi.org/10.1093/bfgp/elv063

    Article  Google Scholar 

  30. Mori H, Baba T, Yokoyama K, Takeuchi R, Nomura W, Makishi K, Otsuka Y, Dose H, Wanner BL (2015) Identification of essential genes and synthetic lethal gene combinations in Escherichia coli K-12. Methods Mol Biol 1279:45–65. https://doi.org/10.1007/978-1-4939-2398-4_4

    Article  Google Scholar 

  31. Nigatu D, Sobetzko P, Yousef M, Henkel W (2017) Sequence-based information-theoretic features for gene essentiality prediction. BMC Bioinform 18(1):1–11. https://doi.org/10.1186/s12859-017-1884-5

    Article  Google Scholar 

  32. Palaniappan K, Mukherjee S (2011) Predicting “essential” genes across microbial genomes: a machine learning approach. In: Proceedings—10th international conference on machine learning and applications, ICMLA 2011, vol 2, pp 189–194. https://doi.org/10.1109/ICMLA.2011.114

  33. Peng C, Gao F (2014) Protein localization analysis of essential genes in prokaryotes. Scientific reports 4. https://doi.org/10.1038/srep06001

  34. Peng C, Lin Y, Luo H, Gao F (2017) A comprehensive overview of online resources to identify and predict bacterial essential genes. Front Microbiol 8:1–13. https://doi.org/10.3389/fmicb.2017.02331

  35. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0

    Article  MATH  Google Scholar 

  36. Sahoo G, Kumar Y (2012) Analysis of parametric & non parametric classifiers for classification technique using weka. Int J Inf Technol Comput Sci (IJITCS) 4(7):43

    Google Scholar 

  37. Seringhaus M, Paccanaro A, Borneman A, Snyder M, Gerstein M (2006) Predicting essential genes in fungal genomes. Genome Res 16(9):1126–1135. https://doi.org/10.1101/gr.5144106

    Article  Google Scholar 

  38. Sobel I, Feldman G (1968) A 3 \(\times \) 3 isotropic gradient operator for image processing. In: Pattern classification and scene analysis. Wiley, pp 271–272

    Google Scholar 

  39. Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118. https://doi.org/10.1016/0893-6080(90)90049-Q

  40. Sun S, Cao Z, Zhu H, Zhao J (2019) A survey of optimization methods from a machine learning perspective. IEEE Trans Cybern 1–14

    Google Scholar 

  41. Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2018) A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, IEEE computer society, pp 6450–6459. https://doi.org/10.1109/CVPR.2018.00675

  42. Veličković P, Karazija L, Lane ND, Bhattacharya S, Liberis E, Liò P, Chieh A, Bellahsen O, Vegreville M (2018) Cross-modal recurrent models for weight objective prediction from multimodal time-series data. In: Proceedings of the 12th EAI international conference on pervasive computing technologies for healthcare, association for computing machinery. New York, NY, USA, PervasiveHealth ’18, pp 178–186. https://doi.org/10.1145/3240925.3240937

  43. Wang J, Peng W, Wu FX (2013) Computational approaches to predicting essential proteins: a survey. Proteomics-Clin Appl 7(1–2):181–192. https://doi.org/10.1002/prca.201200068

    Article  Google Scholar 

  44. Xu L, Guo Z, Liu X (2020) Prediction of essential genes in prokaryote based on artificial neural network. Genes Genomics 42(1):97–106. https://doi.org/10.1007/s13258-019-00884-w

    Article  Google Scholar 

  45. Yang L, Wang J, Wang H, Lv Y, Zuo Y, Li X, Jiang W (2014) Analysis and identification of essential genes in humans using topological properties and biological information. Gene 551(2):138–151. https://doi.org/10.1016/j.gene.2014.08.046

    Article  Google Scholar 

  46. Zeng M, Li M, Wu FX, Li Y, Pan Y (2019) DeepEP: a deep learning framework for identifying essential proteins. BMC Bioinform 20. https://doi.org/10.1186/s12859-019-3076-y

  47. Zhang R, Ou HY, Zhang CT (2004) Deg: a database of essential genes. Nucl Acids Res 32(suppl_1):D271–D272

    Google Scholar 

  48. Zhu J, Gong R, Zhu Q, He Q, Xu N, Xu Y, Cai M, Zhou X, Zhang Y, Zhou M (2018) Genome-wide determination of gene essentiality by transposon insertion sequencing in yeast pichia pastoris. Sci Rep 8(1). https://doi.org/10.1038/s41598-018-28217-z

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Authors and Affiliations

  1. Centro Federal de Educação Tecnológica Celso Suckow da Fonseca—CEFET/RJ, Rio de Janeiro, Brazil

    Kele Belloze, Ribamar Matias, Ivair Luques & Eduardo Bezerra

  2. Universidade Federal de Juiz de Fora—UFJF, Juiz de Fora, Brazil

    Luciana Campos

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  1. Kele Belloze
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  5. Eduardo Bezerra
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Correspondence to Kele Belloze .

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Editors and Affiliations

  1. Scientific Computing Program (PROCC), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. CDTS, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Department of Computational Modeling, National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, Brazil

    Marcelo Trindade dos Santos

  4. Graduate Program in Nanobiosystems, Federal University of Rio de Janeiro, Duque de Caxias, Rio de Janeiro, Brazil

    Francisco José Pereira Lopes

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Belloze, K., Campos, L., Matias, R., Luques, I., Bezerra, E. (2020). A Review of Artificial Neural Networks for the Prediction of Essential Proteins. In: da Silva, F.A.B., Carels, N., Trindade dos Santos, M., Lopes, F.J.P. (eds) Networks in Systems Biology. Computational Biology, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-51862-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-51862-2_4

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