{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:48:49Z","timestamp":1776275329497,"version":"3.50.1"},"reference-count":155,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["S007019N"],"award-info":[{"award-number":["S007019N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007229","name":"Bijzonder Onderzoeksfonds UGent","doi-asserted-by":"publisher","award":["BOF.PDO.2024.0003.01"],"award-info":[{"award-number":["BOF.PDO.2024.0003.01"]}],"id":[{"id":"10.13039\/501100007229","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["1S69520N"],"award-info":[{"award-number":["1S69520N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012331","name":"Agentschap Innoveren en Ondernemen","doi-asserted-by":"publisher","award":["HBC.2020.2292"],"award-info":[{"award-number":["HBC.2020.2292"]}],"id":[{"id":"10.13039\/100012331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002913","name":"Vlaamse Overheid","doi-asserted-by":"publisher","award":["Onderzoeksprogramma Artifici\u00eble Intelligentie"],"award-info":[{"award-number":["Onderzoeksprogramma Artifici\u00eble Intelligentie"]}],"id":[{"id":"10.13039\/501100002913","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002913","name":"Vlaamse Overheid","doi-asserted-by":"publisher","award":["Onderzoeksprogramma Artifici\u00eble Intelligentie"],"award-info":[{"award-number":["Onderzoeksprogramma Artifici\u00eble Intelligentie"]}],"id":[{"id":"10.13039\/501100002913","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>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 exciting 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 HDC\u2019s potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012426","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T17:29:56Z","timestamp":1727198996000},"page":"e1012426","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0903-6061","authenticated-orcid":true,"given":"Michiel","family":"Stock","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2971-5539","authenticated-orcid":true,"given":"Wim","family":"Van Criekinge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7668-2840","authenticated-orcid":true,"given":"Dimitri","family":"Boeckaerts","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2685-4130","authenticated-orcid":true,"given":"Steff","family":"Taelman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7938-1675","authenticated-orcid":true,"given":"Maxime","family":"Van Haeverbeke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4564-1672","authenticated-orcid":true,"given":"Pieter","family":"Dewulf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3876-620X","authenticated-orcid":true,"given":"Bernard","family":"De Baets","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"issue":"6","key":"pcbi.1012426.ref001","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1093\/bib\/bby063","article-title":"A brief history of bioinformatics","volume":"20","author":"J Gauthier","year":"2019","journal-title":"Brief Bioinform"},{"issue":"7553","key":"pcbi.1012426.ref002","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature"},{"key":"pcbi.1012426.ref003","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4057.001.0001","volume-title":"Kernel methods in computational biology","author":"B Sch\u00f6lkopf","year":"2004"},{"issue":"1","key":"pcbi.1012426.ref004","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"JG Greener","year":"2022","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"7873","key":"pcbi.1012426.ref005","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"J Jumper","year":"2021","journal-title":"Nature"},{"issue":"6637","key":"pcbi.1012426.ref006","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Z Lin","year":"2023","journal-title":"Science"},{"issue":"6557","key":"pcbi.1012426.ref007","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"M Baek","year":"2021","journal-title":"Science"},{"issue":"7949","key":"pcbi.1012426.ref008","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1038\/s41586-023-05696-3","article-title":"De novo design of luciferases using deep learning","volume":"614","author":"AHW Yeh","year":"2023","journal-title":"Nature"},{"key":"pcbi.1012426.ref009","doi-asserted-by":"crossref","first-page":"100723","DOI":"10.1016\/j.imu.2021.100723","article-title":"An overview of deep learning in medical imaging","volume":"26","author":"A Anaya-Isaza","year":"2021","journal-title":"Informatics in Medicine Unlocked"},{"issue":"11","key":"pcbi.1012426.ref010","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41589-023-01349-8","article-title":"Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii","volume":"19","author":"G Liu","year":"2023","journal-title":"Nat Chem Biol"},{"issue":"1","key":"pcbi.1012426.ref011","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1038\/s41467-022-29268-7","article-title":"Current progress and open challenges for applying deep learning across the biosciences","volume":"13","author":"N Sapoval","year":"2022","journal-title":"Nat Commun"},{"key":"pcbi.1012426.ref012","first-page":"30","article-title":"Attention is all you need","author":"A Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"pcbi.1012426.ref013","doi-asserted-by":"crossref","DOI":"10.1093\/bioadv\/vbad001","article-title":"Applications of transformer-based language models in bioinformatics: a survey","volume":"3","author":"S Zhang","year":"2023","journal-title":"Bioinformatics. Advances"},{"issue":"12","key":"pcbi.1012426.ref014","first-page":"443","article-title":"The challenges of explainable AI in biomedical data science","volume":"22","author":"H Han","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1012426.ref015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for interpreting and understanding deep neural networks","volume":"73","author":"G Montavon","year":"2018","journal-title":"Digit Signal Process"},{"key":"pcbi.1012426.ref016","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3390\/e26010041","article-title":"Seeing is believing: brain-inspired modular training for mechanistic interpretability","volume":"26","author":"Z Liu","year":"2024","journal-title":"Entropy"},{"issue":"5","key":"pcbi.1012426.ref017","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"C. Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"issue":"6558","key":"pcbi.1012426.ref018","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1126\/science.abe5650","article-title":"Geometric deep learning of RNA structure","volume":"373","author":"RJL Townshend","year":"2021","journal-title":"Science"},{"key":"pcbi.1012426.ref019","doi-asserted-by":"crossref","unstructured":"Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34; 2020. p. 13693\u201313696.","DOI":"10.1609\/aaai.v34i09.7123"},{"issue":"2","key":"pcbi.1012426.ref020","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3390\/technologies11020040","article-title":"A review of deep transfer learning and recent advancements","volume":"11","author":"M Iman","year":"2023","journal-title":"Technologies"},{"key":"pcbi.1012426.ref021","unstructured":"Han S, Pool J, Tran J, Dally WJ. Learning both weights and connections for efficient neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems\u2014Volume 1. NIPS\u201915. Cambridge, MA, USA: MIT Press; 2015. p. 1135\u20131143."},{"key":"pcbi.1012426.ref022","doi-asserted-by":"crossref","unstructured":"Liu J, Zhao H, Ogleari MA, Li D, Zhao J. Processing-in-memory for energy-efficient neural network training: a heterogeneous approach. In: 2018 51st Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). Fukuoka City, Japan; 2018. p. 655\u2013668.","DOI":"10.1109\/MICRO.2018.00059"},{"issue":"2","key":"pcbi.1012426.ref023","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12559-009-9009-8","article-title":"Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors","volume":"1","author":"P. Kanerva","year":"2009","journal-title":"Cogn Comput"},{"issue":"1","key":"pcbi.1012426.ref024","doi-asserted-by":"crossref","first-page":"4911","DOI":"10.1038\/s41467-023-40533-1","article-title":"Toward a formal theory for computing machines made out of whatever physics offers","volume":"14","author":"H Jaeger","year":"2023","journal-title":"Nat Commun"},{"key":"pcbi.1012426.ref025","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/9781119869610.ch2","volume-title":"Advances in Semiconductor Technologies","author":"P. Kanerva","year":"2022"},{"issue":"6","key":"pcbi.1012426.ref026","first-page":"130","article-title":"A survey on hyperdimensional computing aka vector symbolic architectures, Part I: models and data transformations","volume":"55","author":"D Kleyko","year":"2022","journal-title":"ACM Comput Surv"},{"issue":"1\u20132","key":"pcbi.1012426.ref027","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0004-3702(90)90007-M","article-title":"Tensor product variable binding and the representation of symbolic structures in connectionist systems.","volume":"46","author":"P. Smolensky","year":"1990","journal-title":"Artif Intell"},{"issue":"3","key":"pcbi.1012426.ref028","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/72.377968","article-title":"Holographic reduced representations","volume":"6","author":"TA Plate","year":"1995","journal-title":"IEEE Trans Neural Netw"},{"key":"pcbi.1012426.ref029","doi-asserted-by":"crossref","unstructured":"Kanerva P. Binary spatter-coding of ordered k-tuples. In: International Conference on Artificial Neural Networks. Bochum, Germany: Springer; 1996. p. 869\u2013873.","DOI":"10.1007\/3-540-61510-5_146"},{"key":"pcbi.1012426.ref030","first-page":"1","article-title":"Multiplicative binding, representation operators & analogy","author":"RW Gayler","year":"1998","journal-title":"Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences"},{"issue":"6","key":"pcbi.1012426.ref031","doi-asserted-by":"crossref","first-page":"4523","DOI":"10.1007\/s10462-021-10110-3","article-title":"A Comparison of vector symbolic architectures","volume":"55","author":"K Schlegel","year":"2022","journal-title":"Art Intell Rev"},{"key":"pcbi.1012426.ref032","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1613\/jair.1.12664","article-title":"A theoretical perspective on hyperdimensional computing","volume":"72","author":"A Thomas","year":"2021","journal-title":"J Artif Intell Res"},{"issue":"2118","key":"pcbi.1012426.ref033","first-page":"20170237","article-title":"Blessing of dimensionality: mathematical foundations of the statistical physics of data","volume":"376","author":"AN Gorban","year":"2018","journal-title":"Philos Trans A Math Phys Eng Sci"},{"issue":"9","key":"pcbi.1012426.ref034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3558000","article-title":"A survey on hyperdimensional computing aka vector symbolic architectures, part II: Applications, cognitive models, and challenges","volume":"55","author":"D Kleyko","year":"2023","journal-title":"ACM Comput Surv"},{"issue":"2","key":"pcbi.1012426.ref035","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1162\/089976601300014592","article-title":"Binding and normalization of binary sparse distributed representations by context-dependent thinning","volume":"13","author":"DA Rachkovskij","year":"2001","journal-title":"Neural Comput"},{"issue":"20","key":"pcbi.1012426.ref036","first-page":"1","article-title":"Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?","volume":"7","author":"D Bajusz","year":"2015","journal-title":"J Chem"},{"key":"pcbi.1012426.ref037","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1615\/J Automat Inf Scien.v37.i11.60","article-title":"Sparse binary distributed encoding of numeric vectors","volume":"37","author":"D Rachkovskij","year":"2005","journal-title":"J Autom Inform Sci"},{"key":"pcbi.1012426.ref038","first-page":"217","volume-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"P Sutor","year":"2018"},{"issue":"30","key":"pcbi.1012426.ref039","doi-asserted-by":"crossref","DOI":"10.1126\/scirobotics.aaw6736","article-title":"Learning sensorimotor control with neuromorphic sensors: toward hyperdimensional active perception","volume":"4","author":"A Mitrokhin","year":"2019","journal-title":"Science. Robotics"},{"key":"pcbi.1012426.ref040","doi-asserted-by":"crossref","unstructured":"Smith D, Stanford P. A random walk in Hamming space. In: 1990 IJCNN International Joint Conference on Neural Networks. IEEE; 1990. p. 465\u2013470.","DOI":"10.1109\/IJCNN.1990.137756"},{"key":"pcbi.1012426.ref041","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Cano A, Zhuo C, Yin X, Imani M. RegHD: robust and efficient regression in hyper-dimensional learning system. In: 2021 58th ACM\/IEEE Design Automation Conference (DAC). IEEE; 2021. p. 7\u201312.","DOI":"10.1109\/DAC18074.2021.9586284"},{"key":"pcbi.1012426.ref042","article-title":"The hyperdimensional transform for distributional modelling, regression and classification","author":"P Dewulf","year":"2023","journal-title":"arXiv preprint"},{"key":"pcbi.1012426.ref043","doi-asserted-by":"crossref","unstructured":"Imani M, Morris J, Messerly J, Shu H, Deng Y, Rosing T. BRIC: locality-based encoding for energy-efficient brain-inspired hyperdimensional computing. In: Proceedings of the 56th Annual Design Automation Conference 2019. Las Vegas NV USA: ACM; 2019. p. 1\u20136.","DOI":"10.1145\/3316781.3317785"},{"key":"pcbi.1012426.ref044","first-page":"189","volume-title":"Contemporary Mathematics:","author":"WB Johnson","year":"1984"},{"issue":"2","key":"pcbi.1012426.ref045","first-page":"123","article-title":"Randomized algorithms for matrices and data","volume":"3","author":"MMW Mahoney","year":"2011","journal-title":"Found Trends Mach Learn"},{"issue":"6","key":"pcbi.1012426.ref046","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/2842602","article-title":"RandNLA: randomized numerical linear algebra","volume":"59","author":"P Drineas","year":"2016","journal-title":"Commun ACM"},{"issue":"1","key":"pcbi.1012426.ref047","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2559902","article-title":"Sparser Johnson-Lindenstrauss transforms","volume":"61","author":"DM Kane","year":"2014","journal-title":"J ACM"},{"key":"pcbi.1012426.ref048","doi-asserted-by":"crossref","unstructured":"Frady EP, Kleyko D, Kymn CJ, Olshausen BA, Sommer FT. Computing on functions using randomized vector representations. In: NICE \u201822: Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference; 2022.","DOI":"10.1145\/3517343.3522597"},{"key":"pcbi.1012426.ref049","article-title":"The hyperdimensional transform: a holographic representation of functions","author":"P Dewulf","year":"2024","journal-title":"Accepted for IEEE Journal of Selected Topics in Signal Processing"},{"key":"pcbi.1012426.ref050","article-title":"Computing with residue numbers in high-dimensional representation","author":"CJ Kymn","year":"2023","journal-title":"ArXiv"},{"issue":"8","key":"pcbi.1012426.ref051","doi-asserted-by":"crossref","first-page":"3675","DOI":"10.1007\/s00521-019-04397-1","article-title":"Autoscaling Bloom filter: controlling trade-off between true and false positives","volume":"32","author":"D Kleyko","year":"2020","journal-title":"Neural Comput Applic"},{"key":"pcbi.1012426.ref052","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-319-52289-0_21","volume-title":"Quantum Interaction.","author":"A Joshi","year":"2017"},{"key":"pcbi.1012426.ref053","doi-asserted-by":"crossref","first-page":"757125","DOI":"10.3389\/fnins.2022.757125","article-title":"GrapHD: graph-based hyperdimensional memorization for brain-like cognitive learning","volume":"16","author":"P Poduval","year":"2022","journal-title":"Front Neurosci"},{"key":"pcbi.1012426.ref054","doi-asserted-by":"crossref","unstructured":"Nunes I, Heddes M, Givargis T, Nicolau A, Veidenbaum A. GraphHD: efficient graph classification using hyperdimensional computing. In: DATE \u201822: Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe; 2022. p. 1485\u20131490.","DOI":"10.23919\/DATE54114.2022.9774533"},{"key":"pcbi.1012426.ref055","doi-asserted-by":"crossref","unstructured":"Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 30; 2016. p. 1955\u20131961.","DOI":"10.1609\/aaai.v30i1.10314"},{"issue":"24","key":"pcbi.1012426.ref056","doi-asserted-by":"crossref","first-page":"22387","DOI":"10.1007\/s00521-022-07619-1","article-title":"Representation of spatial objects by shift-equivariant similarity-preserving hypervectors","volume":"34","author":"DA Rachkovskij","year":"2022","journal-title":"Neural Comput Applic"},{"key":"pcbi.1012426.ref057","doi-asserted-by":"crossref","unstructured":"Hassan E, Bettayeb M, Mohammad B, Zweiri Y, Saleh H. Hyperdimensional computing versus convolutional neural network: architecture, performance analysis, and hardware complexity. In: 2023 International Conference on Microelectronics (ICM). Abu Dhabi: IEEE; 2023. p. 228\u2013233.","DOI":"10.1109\/ICM60448.2023.10378944"},{"key":"pcbi.1012426.ref058","doi-asserted-by":"crossref","unstructured":"Yilmaz O. Analogy making and logical inference on images using cellular automata based hyperdimensional computing. In: Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches\u2014Volume 1583. COCO\u201915. Aachen, Germany: CEUR-WS.org; 2015. p. 19\u201327.","DOI":"10.1162\/NECO_a_00787"},{"issue":"2","key":"pcbi.1012426.ref059","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MCAS.2020.2988388","article-title":"Classification using hyperdimensional computing: a review","volume":"20","author":"L Ge","year":"2020","journal-title":"IEEE Circ Syst Mag"},{"key":"pcbi.1012426.ref060","doi-asserted-by":"crossref","unstructured":"Frady EP, Kleyko D, Kymn CJ, Olshausen BA, Sommer FT. Computing on functions using randomized vector representations (in brief). In: Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference; 2022. p. 115\u2013122.","DOI":"10.1145\/3517343.3522597"},{"key":"pcbi.1012426.ref061","volume-title":"Vector quantization and signal compression","author":"A Gersho","year":"2012"},{"key":"pcbi.1012426.ref062","first-page":"537","volume-title":"Learning vector quantization.","author":"T. Kohonen","year":"1995"},{"key":"pcbi.1012426.ref063","first-page":"423","volume-title":"Advances in Neural Information Processing Systems","author":"A Sato","year":"1995"},{"key":"pcbi.1012426.ref064","doi-asserted-by":"crossref","unstructured":"Hernandez-Cano A, Matsumoto N, Ping E, Imani M. OnlineHD: robust, efficient, and single-pass online learning using hyperdimensional system. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). Grenoble, France: IEEE; 2021. p. 56\u201361.","DOI":"10.23919\/DATE51398.2021.9474107"},{"key":"pcbi.1012426.ref065","doi-asserted-by":"crossref","unstructured":"Imani M, Morris J, Bosch S, Shu H, De Micheli G, Rosing T. AdaptHD: adaptive efficient training for brain-inspired hyperdimensional computing. In: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). Nara, Japan: IEEE; 2019. p. 1\u20134.","DOI":"10.1109\/BIOCAS.2019.8918974"},{"issue":"1","key":"pcbi.1012426.ref066","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1109\/JPROC.2018.2871163","article-title":"Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of ExG signals","volume":"107","author":"A Rahimi","year":"2018","journal-title":"Proc IEEE"},{"key":"pcbi.1012426.ref067","doi-asserted-by":"crossref","first-page":"100869","DOI":"10.1016\/j.genrep.2020.100869","article-title":"Big data in biology: The hope and present-day challenges in it","volume":"21","author":"S Pal","year":"2020","journal-title":"Gene Reports"},{"issue":"9","key":"pcbi.1012426.ref068","doi-asserted-by":"crossref","first-page":"4327","DOI":"10.1038\/s41596-021-00580-8","article-title":"Rapid ex vivo molecular fingerprinting of biofluids using laser-assisted rapid evaporative ionization mass spectrometry","volume":"16","author":"V Plekhova","year":"2021","journal-title":"Nat Protoc"},{"issue":"12","key":"pcbi.1012426.ref069","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1038\/s41592-022-01673-2","article-title":"Image-seq: spatially resolved single-cell sequencing guided by in situ and in vivo imaging","volume":"19","author":"C Haase","year":"2022","journal-title":"Nat Methods"},{"issue":"7","key":"pcbi.1012426.ref070","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1038\/s41596-018-0008-7","article-title":"High-throughput imaging flow cytometry by optofluidic time-stretch microscopy","volume":"13","author":"C Lei","year":"2018","journal-title":"Nat Protoc"},{"key":"pcbi.1012426.ref071","doi-asserted-by":"crossref","first-page":"97651","DOI":"10.1109\/ACCESS.2021.3059762","article-title":"Hyper-dimensional computing challenges and opportunities for AI applications","volume":"10","author":"E Hassan","year":"2021","journal-title":"IEEE Access"},{"key":"pcbi.1012426.ref072","author":"WA Simon","year":"2022","journal-title":"HDTorch: accelerating hyperdimensional computing with GP-GPUs for design space exploration"},{"issue":"25","key":"pcbi.1012426.ref073","doi-asserted-by":"crossref","first-page":"e2220022120","DOI":"10.1073\/pnas.2220022120","article-title":"Turing, von Neumann, and the computational architecture of biological machines","volume":"120","author":"HM Al-Hashimi","year":"2023","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1012426.ref074","doi-asserted-by":"crossref","unstructured":"Imani M, Bosch S, Javaheripi M, Rouhani B, Wu X, Koushanfar F, et al. SemiHD: semi-supervised learning using hyperdimensional computing. In: 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD); 2019. p. 1\u20138.","DOI":"10.1109\/ICCAD45719.2019.8942165"},{"issue":"8","key":"pcbi.1012426.ref075","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1109\/TC.2020.2992662","article-title":"Accelerating hyperdimensional computing on FPGAs by exploiting computational reuse","volume":"69","author":"S Salamat","year":"2020","journal-title":"IEEE Trans Comput"},{"issue":"6","key":"pcbi.1012426.ref076","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/s41928-020-0410-3","article-title":"In-memory hyperdimensional computing","volume":"3","author":"G Karunaratne","year":"2020","journal-title":"Nat Electron"},{"key":"pcbi.1012426.ref077","doi-asserted-by":"crossref","first-page":"82493","DOI":"10.1109\/ACCESS.2022.3195878","article-title":"Demeter: a fast and energy-efficient food profiler using hyperdimensional computing in memory","volume":"10","author":"T Shahroodi","year":"2022","journal-title":"IEEE Access"},{"key":"pcbi.1012426.ref078","first-page":"1","article-title":"Improved metagenomic analysis with Kraken 2","volume":"20","author":"DE Wood","year":"2019","journal-title":"Genome Biol"},{"issue":"23","key":"pcbi.1012426.ref079","doi-asserted-by":"crossref","first-page":"3740","DOI":"10.1093\/bioinformatics\/btx520","article-title":"MetaCache: context-aware classification of metagenomic reads using minhashing","volume":"33","author":"A M\u00fcller","year":"2017","journal-title":"Bioinformatics"},{"key":"pcbi.1012426.ref080","volume-title":"Interpretable machine learning: a guide for making black box models explainable","author":"C. Molnar","year":"2019"},{"issue":"3","key":"pcbi.1012426.ref081","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions","volume":"41","author":"E \u0160trumbelj","year":"2014","journal-title":"Knowl Inf Syst"},{"key":"pcbi.1012426.ref082","article-title":"Interpretable machine learning for science with PySR and SymbolicRegression.jl","author":"M. Cranmer","year":"2023","journal-title":"arXiv preprint arXiv"},{"issue":"1","key":"pcbi.1012426.ref083","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10462-023-10622-0","article-title":"Interpretable scientific discovery with symbolic regression: a review","volume":"57","author":"N Makke","year":"2024","journal-title":"Art Intell Rev"},{"issue":"2","key":"pcbi.1012426.ref084","doi-asserted-by":"crossref","first-page":"S15","DOI":"10.1186\/s12859-015-0857-9","article-title":"Methods for the integration of multi-omics data: mathematical aspects","volume":"17","author":"M Bersanelli","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"pcbi.1012426.ref085","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab569","article-title":"Multimodal deep learning for biomedical data fusion: a review","volume":"23","author":"SR Stahlschmidt","year":"2022","journal-title":"Brief Bioinform"},{"key":"pcbi.1012426.ref086","doi-asserted-by":"crossref","unstructured":"Chang EJ, Rahimi A, Benini L, Wu AYA. Hyperdimensional computing-based multimodality emotion recognition with physiological signals. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). Hsinchu, Taiwan: IEEE; 2019. p. 137\u2013141.","DOI":"10.1109\/AICAS.2019.8771622"},{"key":"pcbi.1012426.ref087","doi-asserted-by":"crossref","unstructured":"Zhao Q, Yu X, Rosing T. Attentive multimodal learning on sensor data using hyperdimensional computing. In: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks. IPSN \u201823. New York, NY, USA: Association for Computing Machinery; 2023. p. 312\u2013313.","DOI":"10.1145\/3583120.3589824"},{"key":"pcbi.1012426.ref088","author":"K Greff","year":"2020","journal-title":"On the binding problem in artificial neural networks"},{"key":"pcbi.1012426.ref089","doi-asserted-by":"crossref","unstructured":"Neubert P, Schubert S. Hyperdimensional computing as a framework for systematic aggregation of image descriptors. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:2101.07720. arXiv; 2021. p. 16933\u201316942.","DOI":"10.1109\/CVPR46437.2021.01666"},{"issue":"4","key":"pcbi.1012426.ref090","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/s42256-023-00630-8","article-title":"A neuro-vector-symbolic architecture for solving Raven\u2019s progressive matrices","volume":"5","author":"M Hersche","year":"2023","journal-title":"Nat Mach Intell"},{"issue":"20","key":"pcbi.1012426.ref091","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.3390\/electronics12204323","article-title":"A comprehensive review on multiple instance learning","volume":"12","author":"S Fatima","year":"2023","journal-title":"Electronics"},{"key":"pcbi.1012426.ref092","doi-asserted-by":"crossref","unstructured":"Imani M, Nassar T, Rahimi A, Rosing T. HDNA: energy-efficient DNA sequencing using hyperdimensional computing. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI); 2018. p. 271\u2013274.","DOI":"10.1109\/BHI.2018.8333421"},{"key":"pcbi.1012426.ref093","doi-asserted-by":"crossref","unstructured":"Kim Y, Imani M, Moshiri N, Rosing T. GenieHD: efficient DNA pattern matching accelerator using hyperdimensional computing. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). Grenoble, France: IEEE; 2020. p. 115\u2013120.","DOI":"10.23919\/DATE48585.2020.9116397"},{"key":"pcbi.1012426.ref094","doi-asserted-by":"crossref","unstructured":"Zou Z, Chen H, Poduval P, Kim Y, Imani M, Sadredini E, et al. BioHD: an efficient genome sequence search platform using hyperdimensional memorization. In: Proceedings of the 49th Annual International Symposium on Computer Architecture. New York New York: ACM; 2022. p. 656\u2013669.","DOI":"10.1145\/3470496.3527422"},{"key":"pcbi.1012426.ref095","doi-asserted-by":"crossref","unstructured":"Barkam HE, Yun S, Genssler PR, Zou Z, Liu CK, Amrouch H, et al. HDGIM: hyperdimensional genome sequence matching on unreliable highly scaled FeFET. In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE); 2023. p. 1\u20136.","DOI":"10.23919\/DATE56975.2023.10137331"},{"issue":"9","key":"pcbi.1012426.ref096","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3390\/a13090233","article-title":"A brain-inspired hyperdimensional computing approach for classifying massive DNA methylation data of cancer","volume":"13","author":"F Cumbo","year":"2020","journal-title":"Algorithms"},{"issue":"3","key":"pcbi.1012426.ref097","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s12559-024-10258-4","article-title":"Shift-equivariant similarity-preserving hypervector representations of sequences","volume":"16","author":"DA Rachkovskij","year":"2024","journal-title":"Cogn Comput"},{"issue":"6","key":"pcbi.1012426.ref098","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1021\/acs.jproteome.2c00612","article-title":"HyperSpec: ultrafast mass spectra clustering in hyperdimensional space","volume":"22","author":"W Xu","year":"2023","journal-title":"J Proteome Res"},{"key":"pcbi.1012426.ref099","first-page":"90","article-title":"Intelligent processing of proteomics data to predict glioma sensitivity to chemotherapy","volume":"161","author":"DA Rachkovskij","year":"2010","journal-title":"Cybernetics and Computing (In Russian)"},{"key":"pcbi.1012426.ref100","doi-asserted-by":"crossref","unstructured":"Kang J, Xu W, Bittremieux W, Rosing T. Massively parallel open modification spectral library searching with hyperdimensional computing. In: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques. PACT \u201822. New York, NY, USA: Association for Computing Machinery; 2023. p. 536\u2013537.","DOI":"10.1145\/3559009.3569672"},{"key":"pcbi.1012426.ref101","doi-asserted-by":"crossref","unstructured":"Rahimi A, Kanerva P, Rabaey JM. A robust and energy-efficient classifier using brain-inspired hyperdimensional computing. In: Proceedings of the 2016 International Symposium on Low Power Electronics and Design. San Francisco Airport CA USA: ACM; 2016. p. 64\u201369.","DOI":"10.1145\/2934583.2934624"},{"issue":"4","key":"pcbi.1012426.ref102","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1109\/TBCAS.2022.3187944","article-title":"A highly energy-efficient hyperdimensional computing processor for biosignal classification","volume":"16","author":"A Menon","year":"2022","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"pcbi.1012426.ref103","doi-asserted-by":"crossref","first-page":"34403","DOI":"10.1109\/ACCESS.2019.2904311","article-title":"A hyperdimensional computing framework for analysis of cardiorespiratory synchronization during paced deep breathing","volume":"7","author":"D Kleyko","year":"2019","journal-title":"IEEE Access"},{"key":"pcbi.1012426.ref104","doi-asserted-by":"crossref","first-page":"701791","DOI":"10.3389\/fneur.2021.701791","article-title":"A primer on hyperdimensional computing for iEEG seizure detection","volume":"12","author":"KA Schindler","year":"2021","journal-title":"Front Neurol"},{"issue":"1","key":"pcbi.1012426.ref105","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41928-020-00510-8","article-title":"A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition","volume":"4","author":"A Moin","year":"2021","journal-title":"Nat Electron"},{"key":"pcbi.1012426.ref106","doi-asserted-by":"crossref","unstructured":"Moin A, Zhou A, Rahimi A, Benatti S, Menon A, Tamakloe S, et al. An EMG gesture recognition system with flexible high-density sensors and brain-inspired high-dimensional classifier. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE; 2018. p. 1\u20135.","DOI":"10.1109\/ISCAS.2018.8351613"},{"issue":"4","key":"pcbi.1012426.ref107","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/JBHI.2020.3022211","article-title":"An ensemble of hyperdimensional classifiers: Hardware-friendly short-latency seizure detection with automatic iEEG electrode selection","volume":"25","author":"A Burrello","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"pcbi.1012426.ref108","doi-asserted-by":"crossref","unstructured":"Benatti S, Farella E, Gruppioni E, Benini L. Analysis of robust implementation of an EMG pattern recognition based control. In: International Conference on Bio-inspired Systems and Signal Processing. vol. 2. ScitePress; 2014. p. 45\u201354.","DOI":"10.5220\/0004800300450054"},{"key":"pcbi.1012426.ref109","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/OJCAS.2022.3163075","article-title":"Applicability of hyperdimensional computing to seizure detection","volume":"3","author":"L Ge","year":"2022","journal-title":"IEEE Open J Circuits Syst"},{"key":"pcbi.1012426.ref110","doi-asserted-by":"crossref","unstructured":"Watkinson N, Givargis T, Joe V, Nicolau A, Veidenbaum A. Class-modeling of septic shock with hyperdimensional computing. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 1653\u20131659.","DOI":"10.1109\/EMBC46164.2021.9630353"},{"key":"pcbi.1012426.ref111","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fnhum.2018.00014","article-title":"EEG-based brain\u2013computer interfaces for communication and rehabilitation of people with motor impairment: a novel approach of the 21st Century","volume":"12","author":"I Lazarou","year":"2018","journal-title":"Front Hum Neurosci"},{"key":"pcbi.1012426.ref112","first-page":"1147","article-title":"EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor","volume":"16","author":"Z Zou","year":"2022","journal-title":"Front Neurosci"},{"key":"pcbi.1012426.ref113","doi-asserted-by":"crossref","unstructured":"Rahimi A, Benatti S, Kanerva P, Benini L, Rabaey JM. Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition. In: 2016 IEEE International Conference on Rebooting Computing (ICRC). IEEE; 2016. p. 1\u20138.","DOI":"10.1109\/ICRC.2016.7738683"},{"key":"pcbi.1012426.ref114","article-title":"Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection","author":"U Pale","year":"2022","journal-title":"arXiv preprint arXiv:220507654"},{"key":"pcbi.1012426.ref115","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1007\/s11036-017-0942-6","article-title":"Hyperdimensional computing for blind and one-shot classification of EEG error-related potentials","volume":"25","author":"A Rahimi","year":"2020","journal-title":"Mobile Netw Appl"},{"issue":"2","key":"pcbi.1012426.ref116","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TBME.2019.2919137","article-title":"Hyperdimensional computing with local binary patterns: One-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings","volume":"67","author":"A Burrello","year":"2019","journal-title":"IEEE Trans Biomed Eng"},{"key":"pcbi.1012426.ref117","article-title":"Hypervector design for efficient hyperdimensional computing on edge devices","author":"T Basaklar","year":"2021","journal-title":"arXiv preprint arXiv:210306709"},{"key":"pcbi.1012426.ref118","article-title":"Incremental learning in multiple limb positions for electromyography-based gesture recognition using hyperdimensional computing","author":"A Zhou","year":"2021","journal-title":"TechRxiv"},{"key":"pcbi.1012426.ref119","doi-asserted-by":"crossref","unstructured":"Burrello A, Cavigelli L, Schindler K, Benini L, Rahimi A. Laelaps: An energy-efficient seizure detection algorithm from long-term human iEEG recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE; 2019. p. 752\u2013757.","DOI":"10.23919\/DATE.2019.8715186"},{"issue":"3","key":"pcbi.1012426.ref120","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/TBCAS.2015.2477264","article-title":"Low-complexity seizure prediction from iEEG\/sEEG using spectral power and ratios of spectral power","volume":"10","author":"Z Zhang","year":"2015","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"pcbi.1012426.ref121","doi-asserted-by":"crossref","unstructured":"Ni Y, Lesica N, Zeng FG, Imani M. Neurally-inspired hyperdimensional classification for efficient and robust biosignal processing. In: Proceedings of the 41st IEEE\/ACM International Conference on Computer-Aided Design; 2022. p. 1\u20139.","DOI":"10.1145\/3508352.3549477"},{"key":"pcbi.1012426.ref122","doi-asserted-by":"crossref","unstructured":"Burrello A, Schindler K, Benini L, Rahimi A. One-shot learning for iEEG seizure detection using end-to-end binary operations: Local binary patterns with hyperdimensional computing. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE; 2018. p. 1\u20134.","DOI":"10.1109\/BIOCAS.2018.8584751"},{"issue":"3","key":"pcbi.1012426.ref123","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/TBCAS.2019.2914476","article-title":"Online learning and classification of EMG-based gestures on a parallel ultra-low power platform using hyperdimensional computing","volume":"13","author":"S Benatti","year":"2019","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"pcbi.1012426.ref124","doi-asserted-by":"crossref","unstructured":"Pale U, Teijeiro T, Atienza D. Systematic assessment of hyperdimensional computing for epileptic seizure detection. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 6361\u20136367.","DOI":"10.1109\/EMBC46164.2021.9629648"},{"key":"pcbi.1012426.ref125","doi-asserted-by":"crossref","unstructured":"Kleyko D, Osipov E, Wiklund U. Vector-based analysis of the similarity between breathing and heart rate during paced deep breathing. In. Computing in Cardiology Conference (CinC). vol. 45. IEEE. 2018;2018:1\u20134.","DOI":"10.22489\/CinC.2018.146"},{"key":"pcbi.1012426.ref126","doi-asserted-by":"crossref","unstructured":"Guo Y, Imani M, Kang J, Salamat S, Morris J, Aksanli B, et al. Hyperrec: Efficient recommender systems with hyperdimensional computing. In: Proceedings of the 26th Asia and South Pacific Design Automation Conference. Tokyo Odaiba Waterfront, Japan; 2021. p. 384\u2013389.","DOI":"10.1145\/3394885.3431553"},{"key":"pcbi.1012426.ref127","doi-asserted-by":"crossref","unstructured":"Burkhardt HA, Subramanian D, Mower J, Cohen T. Predicting adverse drug-drug interactions with neural embedding of semantic predications. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association; 2019. p. 992.","DOI":"10.1101\/752022"},{"key":"pcbi.1012426.ref128","first-page":"134","article-title":"Distributed representations for the processing of hierarchically structured numerical and symbolic information","volume":"6","author":"SV Slipchenko","year":"2005","journal-title":"System Technologies"},{"key":"pcbi.1012426.ref129","doi-asserted-by":"crossref","unstructured":"Ma D, Thapa R, Jiao X. MoleHD: efficient drug discovery using brain inspired hyperdimensional computing. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas, NV, USA: IEEE; 2022. p. 390\u2013393.","DOI":"10.1109\/BIBM55620.2022.9995708"},{"key":"pcbi.1012426.ref130","doi-asserted-by":"crossref","unstructured":"Watkinson N, Givargis T, Joe V, Nicolau A, Veidenbaum A. Detecting COVID-19 related pneumonia on CT scans using hyperdimensional computing. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 3970\u20133973.","DOI":"10.1109\/EMBC46164.2021.9630898"},{"key":"pcbi.1012426.ref131","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/978-3-642-35659-9_9","volume-title":"Quantum Interaction","author":"T Cohen","year":"2012"},{"issue":"2","key":"pcbi.1012426.ref132","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.jbi.2009.02.002","article-title":"Empirical distributional semantics: methods and biomedical applications","volume":"42","author":"T Cohen","year":"2009","journal-title":"J Biomed Inform"},{"issue":"6","key":"pcbi.1012426.ref133","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1016\/j.jbi.2012.07.003","article-title":"Discovering discovery patterns with predication-based semantic indexing.","volume":"45","author":"T Cohen","year":"2012","journal-title":"J Biomed Inform"},{"key":"pcbi.1012426.ref134","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jbi.2017.03.003","article-title":"Embedding of semantic predications.","volume":"68","author":"T Cohen","year":"2017","journal-title":"J Biomed Inform"},{"issue":"10","key":"pcbi.1012426.ref135","first-page":"1","article-title":"Predicting high-throughput screening results with scalable literature-based discovery methods.","volume":"3","author":"T Cohen","year":"2014","journal-title":"CPT: Pharmacometrics & Systems. Pharmacology"},{"key":"pcbi.1012426.ref136","article-title":"On the opportunities and risks of foundation models","author":"R Bommasani","year":"2022","journal-title":"arXiv preprint arXiv:210807258"},{"issue":"4","key":"pcbi.1012426.ref137","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"J Lee","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1012426.ref138","first-page":"2","article-title":"Domain-ppecific language model pretraining for biomedical natural language processing","volume":"3","author":"Y Gu","year":"2021","journal-title":"ACM Trans Comput Healthcare"},{"key":"pcbi.1012426.ref139","doi-asserted-by":"crossref","unstructured":"Kleyko D, Khan S, Osipov E, Yong SP. Modality classification of medical images with distributed representations based on cellular automata reservoir computing. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE; 2017. p. 1053\u20131056.","DOI":"10.1109\/ISBI.2017.7950697"},{"key":"pcbi.1012426.ref140","doi-asserted-by":"crossref","unstructured":"Billmeyer R, Parhi KK. Biological gender classification from fMRI via hyperdimensional computing. In: 2021 55th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA, USA: IEEE; 2021. p. 578\u2013582.","DOI":"10.1109\/IEEECONF53345.2021.9723179"},{"issue":"5","key":"pcbi.1012426.ref141","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1093\/nar\/gkz1241","article-title":"Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing","volume":"48","author":"LG Nagy","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1012426.ref142","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1146\/annurev-biodatasci-080917-013431","article-title":"Alignment-free sequence analysis and applications","volume":"1","author":"J Ren","year":"2018","journal-title":"Annu Rev Biomed Data Sci"},{"key":"pcbi.1012426.ref143","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1755-7","article-title":"Benchmarking of alignment-free sequence comparison methods","volume":"20","author":"A Zielezinski","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"pcbi.1012426.ref144","doi-asserted-by":"crossref","first-page":"12226","DOI":"10.1038\/s41598-017-12493-2","article-title":"A novel fast vector method for genetic sequence comparison","volume":"7","author":"Y Li","year":"2017","journal-title":"Sci Rep"},{"issue":"3","key":"pcbi.1012426.ref145","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s41579-019-0311-5","article-title":"Phage diversity, genomics and phylogeny","volume":"18","author":"MB Dion","year":"2020","journal-title":"Nat Rev Microbiol"},{"issue":"6","key":"pcbi.1012426.ref146","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1038\/nrm2698","article-title":"The second wave of synthetic biology: from modules to systems","volume":"10","author":"PE Purnick","year":"2009","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"10","key":"pcbi.1012426.ref147","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1109\/JPROC.2022.3209104","article-title":"Vector symbolic architectures as a computing framework for emerging hardware","volume":"110","author":"D Kleyko","year":"2022","journal-title":"Proc IEEE"},{"issue":"1","key":"pcbi.1012426.ref148","first-page":"1981155","article-title":"Prospects and applications of photonic neural networks","volume":"7","author":"C Huang","year":"2022"},{"key":"pcbi.1012426.ref149","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1007\/978-1-4614-1800-9_159","volume-title":"Computational complexity: theory, techniques, and applications","author":"A. Adamatzky","year":"2012"},{"issue":"1","key":"pcbi.1012426.ref150","doi-asserted-by":"crossref","first-page":"12594","DOI":"10.1038\/s41598-022-16874-0","article-title":"Leveraging plant physiological dynamics using physical reservoir computing","volume":"12","author":"O Pieters","year":"2022","journal-title":"Sci Rep"},{"issue":"3","key":"pcbi.1012426.ref151","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/aaai.12065","article-title":"Neurocompositional computing: from the central paradox of cognition to a new generation of AI systems","volume":"43","author":"P Smolensky","year":"2022","journal-title":"AI Magazine"},{"key":"pcbi.1012426.ref152","unstructured":"Chen K, Huang Q, Palangi H, Smolensky P, Forbus K, Gao J. Mapping natural-language problems to formal-language solutions using structured neural representations. In: International Conference on Machine Learning. PMLR; 2020. p. 1566\u20131575."},{"key":"pcbi.1012426.ref153","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/frobt.2020.00063","article-title":"Symbolic representation and learning with hyperdimensional computing","volume":"7","author":"A Mitrokhin","year":"2020","journal-title":"Front Robot AI"},{"key":"pcbi.1012426.ref154","doi-asserted-by":"crossref","unstructured":"Olin-Ammentorp W, Bazhenov M. Bridge networks: relating inputs through vector-symbolic manipulations. In: International Conference on Neuromorphic Systems 2021. ICONS 2021. New York, NY, USA: Association for Computing Machinery; 2021. p. 1\u20136. doi: 10.1145\/3477145.3477161","DOI":"10.1145\/3477145.3477161"},{"key":"pcbi.1012426.ref155","doi-asserted-by":"crossref","unstructured":"Zeman M, Osipov E, Bosni\u0107 Z. Compressed superposition of neural networks for deep learning in edge computing. In: 2021 International Joint Conference on Neural Networks. IEEE; 2021. p. 1\u20138.","DOI":"10.1109\/IJCNN52387.2021.9533602"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T17:30:43Z","timestamp":1727199043000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012426"}},"subtitle":[],"editor":[{"given":"Varun","family":"Dutt","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":155,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012426","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}