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A Novel Differential Essential Genes Prediction Method Based on Random Forests Model

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

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Abstract

Prediction of differential essential genes is an important field to research cell development and differentiation, drug discovery and disease causes. The goal of this work is to extract gene expression and topological changes in biomolecular networks for identifying the essential nodes or modules. Based on the random forests model, this paper proposed an essential node prediction algorithm for biomolecular networks called Differential Network Analysis method based on Random Forests (DNARF). The algorithm had two main points. First, the five-dimension eigenvector construction method was put forward to extract the differential information of nodes in networks. Second, a positive sample expansion method based on the Pearson correlation coefficient was present to solve the problem that positive and negative samples may be unbalanced. In the simulated data experiments, the DNARF algorithm was compared with three other algorithms. The results showed that the DNARF had an excellent performance on the prediction of essential genes. In the real data experiments, four gene regulatory networks were used as datasets. DNARF algorithm predicted five essential genes related to leukemia: HES1, STAT1, TAL1, SPI1 and RFXANK, which had been proved by literatures. Also, DNARF could be applied to other biological networks to identify new essential genes.

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References

  1. Yang, D., Parrish, R.S., Brock, G.N.: Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data. Comput. Biol. Med. 46, 1–10 (2014)

    Article  Google Scholar 

  2. De la Fuente, A.: From ‘differential expression’ to ‘differential networking’–identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326–333 (2010)

    Article  Google Scholar 

  3. Ideker, T., Krogan, N.J.: Differential network biology. Mol. Syst. Biol. 8, 565 (2012)

    Article  Google Scholar 

  4. Hudson, N.J., Reverter, A., Dalrymple, B.P.: A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput. Biol. 5, e1000382 (2009)

    Article  Google Scholar 

  5. Odibat, O., Reddy, C.K.: Ranking differential hubs in gene co-expression networks. J. Bioinform. Comput. Biol. 10, 1240002 (2012)

    Article  Google Scholar 

  6. Chen, Y., Xu, D.: Understanding protein dispensability through machine-learning analysis of high-throughput data. Bioinformatics 21, 575–581 (2004)

    Article  Google Scholar 

  7. Song, J., et al.: PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J. Theor. Biol. 443, 125–137 (2018)

    Article  Google Scholar 

  8. Yeganeh, P.N., Mostafavi, M.T.: Use of machine learning for diagnosis of cancer in ovarian tissues with a selected mRNA panel. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2429–2434. IEEE, Madrid (2018)

    Google Scholar 

  9. Cutler, D.R., et al.: Random forests for classification in ecology. Ecology 88, 2783–2792 (2007)

    Article  Google Scholar 

  10. Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  11. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)

    Article  Google Scholar 

  12. Wuchty, S., Stadler, P.F.: Centers of complex networks. J. Theor. Biol. 223, 45–53 (2003)

    Article  MathSciNet  Google Scholar 

  13. Saramäki, J., Kivelä, M., Onnela, J.-P., Kaski, K., Kertesz, J.: Generalizations of the clustering coefficient to weighted complex networks. Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 75, 027105 (2007)

    Article  Google Scholar 

  14. Glas, A.S., Lijmer, J.G., Prins, M.H., Bonsel, G.J., Bossuyt, P.M.: The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56(11), 1129–1135 (2003)

    Article  Google Scholar 

  15. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  16. Zhang, X.F., Ou-Yang, L., Yan, H.: Incorporating prior information into differential network analysis using nonparanormal graphical models. Bioinformatics 33, 2436 (2017)

    Article  Google Scholar 

  17. Barabási, A.-L.: Scale-free networks: a decade and beyond. Science 325, 412–413 (2009)

    Article  MathSciNet  Google Scholar 

  18. Bockmayr, M., Klauschen, F., Denkert, C., Budczies, J.: New network topology approaches reveal differential correlation patterns;in breast cancer. BMC Syst. Biol. 7, 78 (2013)

    Article  Google Scholar 

  19. Lichtblau, Y., Zimmermann, K., Haldemann, B., Lenze, D., Hummel, M., Leser, U.: Comparative assessment of differential network analysis methods. Brief. Bioinform. 18, 837 (2016)

    Google Scholar 

  20. Neep, S., Stergachis, A.B., Reynolds, A., Sandstrom, R., Borenstein, E., Stamatoyannopoulos, J.A.: Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012)

    Article  Google Scholar 

  21. Stergachis, A.B., et al.: Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515, 365–370 (2014)

    Article  Google Scholar 

  22. Sloan, S.A., et al.: Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron 95, 779–790 (2017)

    Article  Google Scholar 

  23. Hassannia, B., Wiernicki, B., Ingold, I., Qu, F., Berghe, T.V.: Nano-targeted induction of dual ferroptotic mechanisms eradicates high-risk neuroblastoma. J. Clin. Investig. 128, 3341–3355 (2018)

    Article  Google Scholar 

  24. Harris, L.W., et al.: The cerebral microvasculature in schizophrenia: a laser capture microdissection study. PLoS One 3, e3964 (2008)

    Article  Google Scholar 

  25. Mahdavi, M., et al.: Hereditary breast cancer; Genetic penetrance and current status with BRCA. J. Cell. Physiol. 234, 5741–5750 (2019)

    Google Scholar 

  26. Aygun, N., Altungoz, O.: MYCN is amplified during S phase, and c-myb is involved in controlling MYCN expression and amplification in MYCN-amplified neuroblastoma cell lines. Mol. Med. Rep. 19, 345–361 (2019)

    Google Scholar 

  27. Cai, L., et al.: ZFX mediates non-canonical oncogenic functions of the androgen receptor splice variant 7 in castrate-resistant prostate cancer. Mol. Cell 72, 341–354 (2018)

    Article  Google Scholar 

  28. Desmots, F., et al.: Pan-HDAC inhibitors restore PRDM1 response to IL21 in CREBBP-mutated follicular lymphoma. Clin. Cancer Res. 25, 735–746 (2019)

    Article  Google Scholar 

  29. Narayanan, A., et al.: The proneural gene ASCL1 governs the transcriptional subgroup affiliation in glioblastoma stem cells by directly repressing the mesenchymal gene NDRG1. Cell Death Diff. 1 (2018)

    Google Scholar 

  30. García-Martínez, A., et al.: DNA methylation of tumor suppressor genes in pituitary neuroendocrine tumors. J. Clin. Endocrinol. Metabol. 104, 1272–1282 (2018)

    Article  Google Scholar 

  31. Wang, L., et al.: Kaiso (ZBTB33) downregulation by Mirna-181a inhibits cell proliferation, invasion, and the epithelial-mesenchymal transition in glioma cells. Cell. Physiol. Biochem. 48, 947–958 (2018)

    Article  Google Scholar 

  32. Gao, Y., et al.: PPARα Regulates the proliferation of human glioma cells through miR-214 and E2F2. BioMed. Res. Int. 2018 (2018)

    Google Scholar 

  33. Davudian, S., Mansoori, B., Shajari, N., Mohammadi, A., Baradaran, B.: BACH1, the master regulator gene: a novel candidate target for cancer therapy. Gene 588, 30–37 (2016)

    Article  Google Scholar 

  34. Roychoudhuri, R., et al.: The transcription factor BACH2 promotes tumor immunosuppression. J. Clin. Investig. 126, 599–604 (2016)

    Article  Google Scholar 

  35. Blasi, F., Bruckmann, C., Penkov, D., Dardaei, L.: A tale of TALE, PREP1, PBX1, and MEIS1: interconnections and competition in cancer. Bioessays 39 (2017)

    Article  Google Scholar 

  36. Lin, M., Lin, J., Hsu, C., Juan, H., Lou, P., Huang, M.: GATA3 interacts with and stabilizes HIF-1α to enhance cancer cell invasiveness. Oncogene 36, 4243 (2017)

    Article  Google Scholar 

  37. De Bustos, C., Smits, A., Strömberg, B., Collins, V.P., Nistér, M., Afink, G.: A PDGFRA promoter polymorphism, which disrupts the binding of ZNF148, is associated with primitive neuroectodermal tumours and ependymomas. J. Med. Genet. 42, 31–37 (2005)

    Article  Google Scholar 

  38. Richart, L., Real, F.X., Sanchez-Arevalo Lobo, V.J.: c-MYC partners with BPTF in human cancer. Mol. Cell. Oncol. 3, e1152346 (2016)

    Article  Google Scholar 

  39. Rani, A., Greenlaw, R., Smith, R.A., Galustian, C.: HES1 in immunity and cancer. Cytokine Growth Factor Rev. 30, 113–117 (2016)

    Article  Google Scholar 

  40. Kovacic, B., et al.: STAT1 acts as a tumor promoter for leukemia development. Cancer Cell 10, 77–87 (2006)

    Article  Google Scholar 

  41. Brown, L., et al.: Site-specific recombination of the tal-1 gene is a common occurrence in human T cell leukemia. EMBO J. 9, 3343–3351 (1990)

    Article  Google Scholar 

  42. Moreau-Gachelin, F., Ray, D., Tambourin, P., Tavitian, A.: Spi-1 oncogene activation in Rauscher and Friend murine virus-induced acute erythroleukemias. Leukemia 4, 20–23 (1990)

    Google Scholar 

  43. Das, B., Majumder, D.: Information theory based analysis for understanding the regulation of HLA gene expression in human leukemia. Int. J. Infor. Sci. Tech. (2012)

    Google Scholar 

Download references

Acknowledgement

This work was partially supported by the National Natural Science Foundation of China [No. 61873156] and the Project of NSFS [No. 17ZR1409900].

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 99 Shang Da Road, Shanghai, 200444, People’s Republic of China

    Jiang Xie, Jiamin Sun, Jiaxin Li & Fuzhang Yang

  2. Laboratory of Molecular Neural Biology, School of Life Sciences, Shanghai University, 99 Shang Da Road, Shanghai, 200444, People’s Republic of China

    Jiao Wang

  3. Christian Wuhan Britain-China School, Wuhan, 430022, People’s Republic of China

    Haozhe Li

Authors
  1. Jiang Xie
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  2. Jiamin Sun
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  3. Jiaxin Li
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  4. Fuzhang Yang
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  5. Haozhe Li
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  6. Jiao Wang
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Corresponding authors

Correspondence to Jiang Xie or Jiao Wang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Xie, J., Sun, J., Li, J., Yang, F., Li, H., Wang, J. (2019). A Novel Differential Essential Genes Prediction Method Based on Random Forests Model. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_51

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  • DOI: https://doi.org/10.1007/978-3-030-26969-2_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26968-5

  • Online ISBN: 978-3-030-26969-2

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