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Persistent Homology for RNA Data Analysis

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Homology Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2627))

Abstract

Molecular representations are of great importance for machine learning models in RNA data analysis. Essentially, efficient molecular descriptors or fingerprints that characterize the intrinsic structural and interactional information of RNAs can significantly boost the performance of all learning modeling. In this paper, we introduce two persistent models, including persistent homology and persistent spectral, for RNA structure and interaction representations and their applications in RNA data analysis. Different from traditional geometric and graph representations, persistent homology is built on simplicial complex, which is a generalization of graph models to higher-dimensional situations. Hypergraph is a further generalization of simplicial complexes and hypergraph-based embedded persistent homology has been proposed recently. Moreover, persistent spectral models, which combine filtration process with spectral models, including spectral graph, spectral simplicial complex, and spectral hypergraph, are proposed for molecular representation. The persistent attributes for RNAs can be obtained from these two persistent models and further combined with machine learning models for RNA structure, flexibility, dynamics, and function analysis.

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Author information

Authors and Affiliations

  1. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Kelin Xia & JunJie Wee

  2. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Xiang Liu

  3. Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China

    Xiang Liu

Authors
  1. Kelin Xia
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  2. Xiang Liu
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  3. JunJie Wee
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Corresponding author

Correspondence to Kelin Xia .

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Editors and Affiliations

  1. Faculty of Chemistry, University of Warsaw, Warsaw, Poland

    SÅ‚awomir Filipek

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Xia, K., Liu, X., Wee, J. (2023). Persistent Homology for RNA Data Analysis. In: Filipek, S. (eds) Homology Modeling. Methods in Molecular Biology, vol 2627. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2974-1_12

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  • DOI: https://doi.org/10.1007/978-1-0716-2974-1_12

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2973-4

  • Online ISBN: 978-1-0716-2974-1

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