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Hyperdimensional computing in biomedical sciences: a brief review.

Fabio Cumbo¹, Davide Chicco^2,3

¹Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States.

Peerj. Computer Science

|June 26, 2025

Summary

This summary is machine-generated.

Hyperdimensional computing (HDC) offers a novel approach for analyzing biomedical data. This review critically examines HDC

Keywords:

Bioinformatics Biomedical sciences Cheminformatics Hyperdimensional computing Medical informatics Review Vector-symbolic architectures

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Area of Science:

Biomedical Informatics
Bioinformatics
Cheminformatics
Medical Informatics

Background:

Hyperdimensional computing (HDC), also known as vector-symbolic architectures (VSA), is an emerging computational paradigm.
HDC utilizes high-dimensional vectors for information representation and combination.
It has broad applications across scientific disciplines, including machine learning and artificial intelligence.

Purpose of the Study:

To provide a critical and comprehensive review of the current HDC landscape in biomedical sciences.
To highlight the advantages and disadvantages of applying HDC in bioinformatics, cheminformatics, and medical informatics.
To identify potential future directions and challenges for HDC adoption in these domains.

Main Methods:

Systematic review of approximately forty scientific articles on HDC applied to biomedical data.
Analysis of key aspects including vector construction, data encoding, and programming languages used.
Assessment of open access status, software code availability, and prominent research groups, journals, and conferences.

Main Results:

Identified key features and methodologies in current HDC applications within biomedical sciences.
Quantified the prevalence of open access publications and publicly available software code.
Summarized the distribution of research contributions across authors, journals, and conferences.

Conclusions:

HDC presents a promising computational paradigm for biomedical data analysis.
The review outlines the current state, pros, and cons of HDC in specific biomedical domains.
This work serves as the first open survey, offering valuable insights for researchers in biomedical sciences.