Hyperdimensional computing: a fast, robust and interpretable paradigm
  for biological data
Michiel Stock Dimitri Boeckaerts Pieter Dewulf Steff Taelman Maxime Van Haeverbeke Wim Van Criekinge Bernard De Baets
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.