{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,4]],"date-time":"2024-08-04T04:43:42Z","timestamp":1722746622175},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,8,15]]},"abstract":"Abstract<\/jats:title>\n Motivation: With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions; in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng;nsio with nontrivial topological features. As the first step in establishing such a trend, we present muxstep, an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described.<\/jats:p>\n Availability and Implementation: The full source code is available on GitHub: https:\/\/github.com\/PetarV-\/muxstep<\/jats:p>\n Contact: \u00a0petar.velickovic@cl.cam.ac.uk<\/jats:p>\n Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw196","type":"journal-article","created":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T01:25:59Z","timestamp":1460597159000},"page":"2562-2564","source":"Crossref","is-referenced-by-count":2,"title":["Muxstep: an open-source C\u2009++ multiplex HMM library for making inferences on multiple data types"],"prefix":"10.1093","volume":"32","author":[{"given":"Petar","family":"Veli\u010dkovi\u0107","sequence":"first","affiliation":[{"name":"Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Li\u00f2","sequence":"additional","affiliation":[{"name":"Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,4,13]]},"reference":[{"key":"2023020112585036700_btw196-B1","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1038\/srep01344","article-title":"Emergence of network features from multiplexity","volume":"3","author":"Cardillo","year":"2013","journal-title":"Scientific Reports"},{"key":"2023020112585036700_btw196-B2","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevX.5.011027","article-title":"Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems","volume":"5","author":"De Domenico","year":"2015","journal-title":"Physical Review X"},{"key":"2023020112585036700_btw196-B3","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"2023020112585036700_btw196-B4","doi-asserted-by":"crossref","first-page":"1344.","DOI":"10.1103\/PhysRevE.90.012808","article-title":"Competing spreading processes on multiplex networks:awareness and epidemics","volume":"90","author":"Granell","year":"2014","journal-title":"Physical Review E"},{"key":"2023020112585036700_btw196-B5","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/comnet\/cnu016","article-title":"Multilayer networks","volume":"2","author":"Kivel\u00e4","year":"2014","journal-title":"Journal of Complex Networks"},{"key":"2023020112585036700_btw196-B6","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/bib\/bbk007","article-title":"Machine learning in bioinformatics","volume":"7","author":"Larra\u00f1aga","year":"2006","journal-title":"Briefings in Bioinformatics"},{"issue":"suppl. 1","key":"2023020112585036700_btw196-B7","first-page":"D539","article-title":"BioGRID: a general repository for interaction datasets","volume":"34","author":"Stark","year":"2006","journal-title":"Nucleic Acids Research"},{"key":"2023020112585036700_btw196-B8","doi-asserted-by":"crossref","DOI":"10.1093\/comnet\/cnv029","article-title":"Molecular multiplex network inference using Gaussian mixture hidden Markov models","author":"Veli\u010dkovi\u0107","year":"2015","journal-title":"Journal of Complex Networks Advance Access"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/16\/2562\/49020727\/bioinformatics_32_16_2562.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/16\/2562\/49020727\/bioinformatics_32_16_2562.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T22:58:29Z","timestamp":1675292309000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/16\/2562\/1743536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,13]]},"references-count":8,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2016,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btw196","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,8,15]]},"published":{"date-parts":[[2016,4,13]]}}}