Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914693079105536 |
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| author | Stock, Michiel Boeckaerts, Dimitri Dewulf, Pieter Taelman, Steff Van Haeverbeke, Maxime Van Criekinge, Wim De Baets, Bernard |
| author_facet | Stock, Michiel Boeckaerts, Dimitri Dewulf, Pieter Taelman, Steff Van Haeverbeke, Maxime Van Criekinge, Wim De Baets, Bernard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17572 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data Stock, Michiel Boeckaerts, Dimitri Dewulf, Pieter Taelman, Steff Van Haeverbeke, Maxime Van Criekinge, Wim De Baets, Bernard Machine Learning Quantitative Methods 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. |
| title | Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2402.17572 |