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

Autor: Stock, Michiel, Boeckaerts, Dimitri, Dewulf, Pieter, Taelman, Steff, Van Haeverbeke, Maxime, Van Criekinge, Wim, De Baets, Bernard

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 b

Externí odkaz: http://arxiv.org/abs/2402.17572

The Hyperdimensional Transform: a Holographic Representation of Functions

Autor: Dewulf, Pieter, Stock, Michiel, De Baets, Bernard

Integral transforms are invaluable mathematical tools to map functions into spaces where they are easier to characterize. We introduce the hyperdimensional transform as a new kind of integral transform. It converts square-integrable functions into no

Externí odkaz: http://arxiv.org/abs/2310.16065

Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.

Autor: De Brouwer, Edward, Becker, Thijs, Werthen-Brabants, Lorin, Dewulf, Pieter, Iliadis, Dimitrios, Dekeyser, Cathérine, Laureys, Guy, Van Wijmeersch, Bart, Popescu, Veronica, Dhaene, Tom, Deschrijver, Dirk, Waegeman, Willem, De Baets, Bernard, Stock, Michiel, Horakova, Dana, Patti, Francesco, Izquierdo, Guillermo, Eichau, Sara, Girard, Marc, Prat, Alexandre

Publikováno v: PLoS Digital Health; 7/25/2024, Vol. 3 Issue 7, p1-25, 25p

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

Autor: Stock M; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium., Van Criekinge W; Biobix Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium., Boeckaerts D; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.; Laboratory of Applied Biotechnology, Department of Biotechnology, Ghent University, Ghent, Belgium., Taelman S; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.; Biobix Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.; BioLizard nv, Ghent, Belgium., Van Haeverbeke M; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium., Dewulf P; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium., De Baets B; KERMIT Research Unit, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.

Publikováno v: PLoS computational biology [PLoS Comput Biol] 2024 Sep 24; Vol. 20 (9), pp. e1012426. Date of Electronic Publication: 2024 Sep 24 (Print Publication: 2024).