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Recent Advances of Data Biclustering with Application in Computational Neuroscience

In: Computational Neuroscience

Author

Listed:
  • Neng Fan

    (University of Florida)

  • Nikita Boyko

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Clustering and biclustering are important techniques arising in data mining. Different from clustering, biclustering simultaneously groups the objects and features according their expression levels. In this review, the backgrounds, motivation, data input, objective tasks, and history of data biclustering are carefully studied. The bicluster types and biclustering structures of data matrix are defined mathematically. Most recent algorithms, including OREO, nsNMF, BBC, cMonkey, etc., are reviewed with formal mathematical models. Additionally, a match score between biclusters is defined to compare algorithms. The application of biclustering in computational neuroscience is also reviewed in this chapter.

Suggested Citation

  • Neng Fan & Nikita Boyko & Panos M. Pardalos, 2010. "Recent Advances of Data Biclustering with Application in Computational Neuroscience," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Panos M. Pardalos & Petros Xanthopoulos (ed.), Computational Neuroscience, chapter 0, pages 85-112, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88630-5_6
    DOI: 10.1007/978-0-387-88630-5_6
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    Cited by:

    1. Li, Gen, 2020. "Generalized Co-clustering Analysis via Regularized Alternating Least Squares," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    2. Amir Lakizadeh & Saeed Jalili, 2016. "BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.

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