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Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data

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  • Michiel Stock
  • Wim Van Criekinge
  • Dimitri Boeckaerts
  • Steff Taelman
  • Maxime Van Haeverbeke
  • Pieter Dewulf
  • Bernard De Baets

Abstract

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an exciting alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning, or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores HDC’s potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications.

Suggested Citation

  • Michiel Stock & Wim Van Criekinge & Dimitri Boeckaerts & Steff Taelman & Maxime Van Haeverbeke & Pieter Dewulf & Bernard De Baets, 2024. "Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data," PLOS Computational Biology, Public Library of Science, vol. 20(9), pages 1-23, September.
  • Handle: RePEc:plo:pcbi00:1012426
    DOI: 10.1371/journal.pcbi.1012426
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    1. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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