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

• URL: http://arxiv.org/abs/2402.17572v1
• Date: Tue, 27 Feb 2024 15:09:20 GMT
• Title: Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
• Authors: Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf, Steff Taelman, Maxime Van Haeverbeke, Wim Van Criekinge, Bernard De Baets
• Abstract summary: New algorithms for processing diverse biological data sources have revolutionized bioinformatics. Deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. Hyperdimensional computing has emerged as an intriguing alternative.
• Score: 9.094234519404907
• License: http://creativecommons.org/licenses/by/4.0/
• 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 intriguing 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 the potential of HDC for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds a lot of potential for various omics data searching, biosignal analysis and health applications.

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