12 papers · ranked by Valyu relevance
Michiel Stock, Wim Van Criekinge, Dimitri Boeckaerts, Steff Taelman + 4 more
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…
Fabio Cumbo, Davide Chicco, Bilal Alatas
Hyperdimensional computing (HDC, also known as vector-symbolic architectures-VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. It finds applications in a wide array of fields including bioinformatics, natural…
Xun Jiao, Abbas Rahimi, Cornelia Fermüller, John Yiannis Aloimonos
The field of Hyperdimensional (HD) Computing, a.k.a. Vector Symbolic Architectures (VSA), is founded on the notion that the mind can be modeled by computing with high-dimensional vectors. Such vectors capture well the phenomena apparent in the ensemble activity of large populations of neurons in the brain. HD Computing…
Prathyush Poduval, Haleh Alimohamadi, Ali Zakeri, Farhad Imani + 3 more
'M. Hassan Najafi' 'Tony Givargis' 'Mohsen Imani'] Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their…
Kaspar A. Schindler, Abbas Rahimi
A central challenge in today's care of epilepsy patients is that the disease dynamics are severely under-sampled in the currently typical setting with appointment-based clinical and electroencephalographic examinations. Implantable devices to monitor electrical brain signals and to detect epileptic seizures may…
Fabio Cumbo, Kabir Dhillon, Jayadev Joshi, Bryan Raubenolt + 3 more
Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and…
Jayadev Joshi, Fabio Cumbo, Daniel Blankenberg
Classical machine learning techniques have revolutionized bioinformatics, enabling researchers to extract knowledge from complex biological data. However, these techniques often struggle with high-dimensional data, where the increasing number of features leads to decreased performance, also affecting models accuracy.…
Jayadev Joshi, Fabio Cumbo, Daniel Blankenberg
Classical machine learning techniques have revolutionized bioinformatics, enabling researchers to extract knowledge from complex biological data. However, these techniques often struggle with high-dimensional data, where the increasing number of features leads to decreased performance, also affecting models accuracy.…
Christopher J. Kymn, Denis Kleyko, E. Paxon Frady, Connor Bybee + 3 more
'Pentti Kanerva' 'Friedrich T. Sommer' 'Bruno A. Olshausen'] We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show how residue numbers can be represented as high-dimensional vectors in a manner…
Fabio Cumbo, Kabir Dhillon, M. Hassan Najafi, Sercan Aygun + 1 more
The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch…
Chengting Yu, Yujie Wu, Aili Wang, Wolfgang Maass
Hyperdimensional computing (HDC) addresses massively parallel implementations of symbolic computations that are both more transparent than ANNs and LLMs and more suitable for in-memory computing on highly energy-efficient analog hardware. It captures an essential aspects of brain computations: objects, concepts, and…
Weihong Xu, Po-Kai Hsu, Niema Moshiri, Shimeng Yu + 1 more
Genomic distance estimation is a critical workload since exact computation for whole-genome similarity metrics such as Average Nucleotide Identity (ANI) incurs exhibitive runtime overhead. Genome sketching is a fast and memory-efficient solution to estimate ANI similarity by distilling representative k-mers from the…