Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data

arXiv:2402.17572v1 Announce Type: new
Abstract: 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 …

abstract advances algorithms alignment arxiv bioinformatics computing cs.lg data data sources deep learning diverse functional paradigm pivotal processing q-bio.qm robust type