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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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| 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. |
| format | Article |
| id | doaj-art-07e042c3706f4602bf15fffaf1fb7b37 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| 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 AT davidechicco hyperdimensionalcomputinginbiomedicalsciencesabriefreview |