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A topological data analysis based classification method for multiple measurements

dc.contributor.authorRiihimäki, Henri
dc.contributor.authorChachólski, Wojciech
dc.contributor.authorTheorell, Jakob
dc.contributor.authorHillert, Jan
dc.contributor.authorRamanujam, Ryan
dc.date.accessioned2020-09-01T08:10:02Z
dc.date.available2020-09-01T08:10:02Z
dc.date.issued2020-07-29
dc.identifier.citationRiihimäki , H , Chachólski , W , Theorell , J , Hillert , J & Ramanujam , R 2020 , ' A topological data analysis based classification method for multiple measurements ' , BMC Bioinformatics , vol. 21 , 336 . https://doi.org/10.1186/s12859-020-03659-3en
dc.identifier.issn1471-2105
dc.identifier.otherPURE: 175012510
dc.identifier.otherPURE UUID: 62e79482-c4fe-4b99-9c0a-ce4d8e55b305
dc.identifier.otherRIS: urn:239823C9244D4A497C335371DDA0FC09
dc.identifier.otherRIS: Riihimäki2020
dc.identifier.otherScopus: 85088852643
dc.identifier.otherPubMed: 32727348
dc.identifier.otherWOS: 000559095100002
dc.identifier.urihttps://hdl.handle.net/2164/15044
dc.descriptionHR was partly supported by a collaboration agreement between the University of Aberdeen and EPFL. WC was partially supported by VR 2014-04770 and Wallenberg AI, Autonomous System and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, Göran Gustafsson Stiftelse. JT is fully funded by the Wenner-Gren Foundation. JH is partially supported by VR K825930053. RR is partially supported by MultipleMS. The collaboration agreement between EPFL and University of Aberdeen played a role in the design of the neuron spiking analysis and in providing the data required, i.e. the neuronal network and the spiking activity. Open access funding provided by Karolinska Institute.en
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofBMC Bioinformaticsen
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en
dc.subjectTopological data analysisen
dc.subjectmachine learningen
dc.subjectmultiple measurement analysisen
dc.subjectMachine learningen
dc.subjectMultiple measurement analysisen
dc.subjectTrees/anatomy & histologyen
dc.subjectHumansen
dc.subjectRatsen
dc.subjectSupport Vector Machineen
dc.subjectMachine Learningen
dc.subjectAlgorithmsen
dc.subjectAnimalsen
dc.subjectLasersen
dc.subjectComputer Simulationen
dc.subjectData Analysisen
dc.subjectQA Mathematicsen
dc.subjectApplied Mathematicsen
dc.subjectMolecular Biologyen
dc.subjectStructural Biologyen
dc.subjectBiochemistryen
dc.subjectComputer Science Applicationsen
dc.subject.lccQAen
dc.titleA topological data analysis based classification method for multiple measurementsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Mathematical Scienceen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1186/s12859-020-03659-3
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85088852643&partnerID=8YFLogxKen
dc.identifier.vol21en


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