Riihimäki, HenriChachólski, WojciechTheorell, JakobHillert, JanRamanujam, Ryan2020-09-012020-09-012020-07-29Riihimäki, 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/s12859-020-03659-31471-2105RIS: urn:239823C9244D4A497C335371DDA0FC09RIS: Riihimäki2020https://hdl.handle.net/2164/15044HR 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.182486352engTopological data analysismachine learningmultiple measurement analysisMachine learningMultiple measurement analysisTrees/anatomy & histologyHumansRatsSupport Vector MachineMachine LearningAlgorithmsAnimalsLasersComputer SimulationData AnalysisQA MathematicsApplied MathematicsMolecular BiologyStructural BiologyBiochemistryComputer Science ApplicationsQAJournal article10.1186/s12859-020-03659-3http://www.scopus.com/inward/record.url?scp=85088852643&partnerID=8YFLogxK21