Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要