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Simultaneous Clustering of Multiple Gene Expression Datasets for Pattern Discovery

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Advances in Artificial Intelligence, Computation, and Data Science

Part of the book series: Computational Biology ((COBO,volume 31))

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Background

Healthy cells run sophisticated genetic programmes in order to carry out their various biological processes such as cellular growth, cell division, stress response, and metabolism. The regulation of these genetic programmes is realised at different levels by controlling the production of the required types of large biomolecules such as RNAs, proteins, glycans, and lipids, with different amounts, at different times, and in different sub-cellular locations. Although all cells in an organism, such as skin cells, liver cells, bone cells, and neurons nominally have the same genomic material, they differ in shape and function because of the differences in the genetic programmes that they run.

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Authors and Affiliations

  1. Sensyne Health plc, Oxford, UK

    Basel Abu-Jamous

  2. Phi Science, Amman, Jordan

    Basel Abu-Jamous

  3. Electronic and Electrical Engineering, Brunel University London, Uxbridge, UK

    Asoke K. Nandi

Authors
  1. Basel Abu-Jamous
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  2. Asoke K. Nandi
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Corresponding author

Correspondence to Asoke K. Nandi .

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Editors and Affiliations

  1. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Tuan D. Pham

  2. Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong

    Hong Yan

  3. College of Sciences and Human Studies, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Muhammad W. Ashraf

  4. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

    Folke Sjöberg

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Abu-Jamous, B., Nandi, A.K. (2021). Simultaneous Clustering of Multiple Gene Expression Datasets for Pattern Discovery. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-69951-2_4

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