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.

翻译：生物信息学的进展主要归功于用于处理多种生物数据源的新算法。尽管复杂的比对算法在分析生物序列中发挥了关键作用，但深度学习已显著改变了生物信息学，涉及序列、结构和功能分析。然而，这些方法极其依赖数据、计算密集且难以解释。超维计算（HDC）最近作为一种引人注目的替代方案出现。其核心思想是：高维随机向量可以表示序列特征或系统发育等概念。这些向量可以通过简单算子组合，利用高维空间的特殊性质进行学习、推理或查询。我们的工作综述并探索了HDC在生物信息学中的潜力，强调其高效性、可解释性以及处理多模态和结构化数据的适应性。HDC在全组学数据搜索、生物信号分析和健康应用中具有广阔前景。