Advertisement

Classification of Blood Cancer and Form Associated Gene Networks Using Gene Expression Profiles

  • Tejal UpadhyayEmail author
  • Samir Patel
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)

Abstract

Blood cells are produced at bone marrow called the soft, spongy center of bones. Leukemia is a one type of cancer which occurs either at blood or at bone marrow. It can happen when there is a problem with the production of blood cells. It usually affects the leukocytes or white blood cells. Once the blood cancer develops, the body produces huge amount of abnormal blood cells. In most varieties of Leukemia, the abnormal cells are white blood cells and they look completely different from traditional blood cells. In this paper, the categories of Leukemia are briefly justified, the method shows a robust performance applied to patient-based gene expression datasets. In this article, we have taken 60 microarray samples from the patient’s bone marrow and that samples are of four different types: ALL, AML, CLL, and AML with non-leukemia also. The article projected associate algorithmic rule to make clear classifier associated gene networks supported genome-wide expression knowledge. The input for this algorithmic rule is the Expression Set or Expression Matrix of the samples and output provides three completely different categories such as Gene Ranking, Classifier, and gene Network associated to every class.

Keywords

Leukemia geNetClassfier Gene ranking Classifier 

References

  1. 1.
    Cheson, B.D., Bennett, J.M., Grever, M., Kay, N., Keating, M.J., O’Brien, S., Rai, K.R.: National cancer institute-sponsored working group guidelines for chronic lymphocytic leukemia: revised guidelines for diagnosis and treatment. Blood 87(12), 4990–4997 (1996)Google Scholar
  2. 2.
    Bohlke, K.: A basic guide to understanding leukemia. Group (2017)Google Scholar
  3. 3.
    Hutter, J.J.: Childhood leukemia. Pediatr. Rev. 31(6), 234 (2010)CrossRefGoogle Scholar
  4. 4.
    Su, C.-L., Deng, T.-R., Shang, Z., Xiao, Y.: Jarid2 inhibits leukemia cell proliferation by regulating ccnd1 expression. Int. J. Hematol. 102(1), 76–85 (2015)CrossRefGoogle Scholar
  5. 5.
    Vardiman, J.W., Thiele, J., Arber, D.A., Brunning, R.D., Borowitz, M.J., Porwit, A., Harris, N.L., Le Beau, M.M., Hellström-Lindberg, E., Tefferi, A., et al.: The 2008 revision of the world health organization (who) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114(5), 937–951 (2009)CrossRefGoogle Scholar
  6. 6.
    Baba, A.I., Câtoi, C.: Comparative Oncology. Publishing House of the Romanian Academy Bucharest (2007)Google Scholar
  7. 7.
    Kendziorski, C., Newton, M., Lan, H., Gould, M.: On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Stat. Med. 22(24), 3899–3914 (2003)CrossRefGoogle Scholar
  8. 8.
    Morris, C.N.: Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78(381), 47–55 (1983)MathSciNetCrossRefGoogle Scholar
  9. 9.
    W.H. Organization and W. H. O. M. of Substance Abuse Unit: Global Status Report on Alcohol and Health, 2014. World Health Organization (2014)Google Scholar
  10. 10.
    Aibar, S., Fontanillo, C., De Las Rivas, J.: Genetclassifier: classify diseases and build associated gene networks using gene expression profiles (2013)Google Scholar
  11. 11.
  12. 12.
    Barrier, A., Lemoine, A., Boelle, P.-Y., Tse, C., Brault, D., Chiappini, F., Breittschneider, J., Lacaine, F., Houry, S., Huguier, M., et al.: Colon cancer prognosis prediction by gene expression profiling. Oncogene 24(40), 6155 (2005)CrossRefGoogle Scholar
  13. 13.
    Meyer, P.E., Lafitte, F., Bontempi, G.: Minet: Ar/bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinf. 9(1), 461 (2008)CrossRefGoogle Scholar
  14. 14.
    Winkler, U., Jensen, M., Manzke, O., Schulz, H., Diehl, V., Engert, A.: Cytokine-release syndrome in patients with b-cell chronic lymphocytic leukemia and high lymphocyte counts after treatment with an anti-cd20 monoclonal antibody (rituximab, idec-c2b8). Blood 94(7), 2217–2224 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Nirma UniversityAhmedabadIndia
  2. 2.Pandit Dindayal Petrolium UniversityĜandhinagarIndia

Personalised recommendations