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Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
Feb. 28, 2024, 5:42 a.m. | Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf, Steff Taelman, Maxime Van Haeverbeke, Wim Van Criekinge, Bernard De Baets
cs.LG updates on arXiv.org arxiv.org
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
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