Hyperdimensional computing in biomedical sciences: a brief review

Hyperdimensional computing (HDC, also known as vector-symbolic architectures—VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. It finds applications in a wide array of fields including bioinf...

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Main Authors: Fabio Cumbo, Davide Chicco
Format: Article
Language:English
Published: PeerJ Inc. 2025-05-01
Series:PeerJ Computer Science
Subjects:
Online Access:https://peerj.com/articles/cs-2885.pdf
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author Fabio Cumbo
Davide Chicco
author_facet Fabio Cumbo
Davide Chicco
author_sort Fabio Cumbo
collection DOAJ
description Hyperdimensional computing (HDC, also known as vector-symbolic architectures—VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. It finds applications in a wide array of fields including bioinformatics, natural language processing, machine learning, artificial intelligence, and many other scientific disciplines. Here we introduced the basic foundations of the HDC, focusing on its application to biomedical sciences, with a particular emphasis to bioinformatics, cheminformatics, and medical informatics, providing a critical and comprehensive review of the current HDC landscape, highlighting pros and cons of applying this computational paradigm in these specific scientific domains. In this study, we first selected around forty scientific articles on hyperdimensional computing applied to biomedical data existing in the literature, and then analyzed key aspects of their studies, such as vector construction, data encoding, programming language employed, and other features. We also counted how many of these scientific articles are open access, how many have public software code available, how many groups of authors, journals, and conferences are most present among them. Finally, we discussed the advantages and limitations of the HDC approach, outlining potential future directions and open challenges for the adoption of HDC in biomedical sciences. To the best of our knowledge, our review is the first open brief survey on this topic among the biomedical sciences, and therefore we believe it can be of interest and useful for the readership.
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spelling doaj-art-07e042c3706f4602bf15fffaf1fb7b372025-08-20T01:50:57ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e288510.7717/peerj-cs.2885Hyperdimensional computing in biomedical sciences: a brief reviewFabio Cumbo0Davide Chicco1Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United StatesDipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, ItalyHyperdimensional computing (HDC, also known as vector-symbolic architectures—VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. It finds applications in a wide array of fields including bioinformatics, natural language processing, machine learning, artificial intelligence, and many other scientific disciplines. Here we introduced the basic foundations of the HDC, focusing on its application to biomedical sciences, with a particular emphasis to bioinformatics, cheminformatics, and medical informatics, providing a critical and comprehensive review of the current HDC landscape, highlighting pros and cons of applying this computational paradigm in these specific scientific domains. In this study, we first selected around forty scientific articles on hyperdimensional computing applied to biomedical data existing in the literature, and then analyzed key aspects of their studies, such as vector construction, data encoding, programming language employed, and other features. We also counted how many of these scientific articles are open access, how many have public software code available, how many groups of authors, journals, and conferences are most present among them. Finally, we discussed the advantages and limitations of the HDC approach, outlining potential future directions and open challenges for the adoption of HDC in biomedical sciences. To the best of our knowledge, our review is the first open brief survey on this topic among the biomedical sciences, and therefore we believe it can be of interest and useful for the readership.https://peerj.com/articles/cs-2885.pdfHyperdimensional computingVector-symbolic architecturesBiomedical sciencesBioinformaticsMedical informaticsCheminformatics
spellingShingle Fabio Cumbo
Davide Chicco
Hyperdimensional computing in biomedical sciences: a brief review
PeerJ Computer Science
Hyperdimensional computing
Vector-symbolic architectures
Biomedical sciences
Bioinformatics
Medical informatics
Cheminformatics
title Hyperdimensional computing in biomedical sciences: a brief review
title_full Hyperdimensional computing in biomedical sciences: a brief review
title_fullStr Hyperdimensional computing in biomedical sciences: a brief review
title_full_unstemmed Hyperdimensional computing in biomedical sciences: a brief review
title_short Hyperdimensional computing in biomedical sciences: a brief review
title_sort hyperdimensional computing in biomedical sciences a brief review
topic Hyperdimensional computing
Vector-symbolic architectures
Biomedical sciences
Bioinformatics
Medical informatics
Cheminformatics
url https://peerj.com/articles/cs-2885.pdf
work_keys_str_mv AT fabiocumbo hyperdimensionalcomputinginbiomedicalsciencesabriefreview
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