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Biomedical Data Ensemble Classification using Random Projections
dc.creator | Tasoulis S.K., Vrahatis A.G., Georgakopoulos S.V., Plagianakos V.P. | en |
dc.date.accessioned | 2023-01-31T10:06:56Z | |
dc.date.available | 2023-01-31T10:06:56Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1109/BigData.2018.8622606 | |
dc.identifier.isbn | 9781538650356 | |
dc.identifier.uri | http://hdl.handle.net/11615/79633 | |
dc.description.abstract | Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which are generated by emerged medical imaging, sensor technologies and high-throughput technologies. An indicative example is the single cell sequencing technology which concerns the genome sequencing examination of hundreds of separate cells in a single tumor. Consequently, open challenges arising from this emerged technology and generally from the evolution of biomedical technologies under the big data perspective. Also, given the fact that approaches based on high-performance computing require high computing resources and advanced developers, solutions that reduce the problem complexity remain very attractive. Following this direction, in this paper a classification scheme based on Multiple Random Projections and Voting is presented. Random Projections offer a platform not only for a low computational time analysis by significantly reducing the data dimensionality, but also for an accurate analysis which may well exceed classical classification approaches. The proposed method was applied on real biomedical high dimensional data and compared against well-known classification schemes as to Random Projection-based cutting-edge methods. Specifically, we applied it on expression profiles for single-cell RNA-seq data from non-diabetic and type 2 diabetic human samples. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for biomedical Big Data analysis and knowledge discovery. © 2018 IEEE. | en |
dc.language.iso | en | en |
dc.source | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062635217&doi=10.1109%2fBigData.2018.8622606&partnerID=40&md5=20c89a115455eb5370ac963ef19a08ef | |
dc.subject | Big data | en |
dc.subject | Cells | en |
dc.subject | Classification (of information) | en |
dc.subject | Clustering algorithms | en |
dc.subject | Cytology | en |
dc.subject | Data mining | en |
dc.subject | Medical imaging | en |
dc.subject | RNA | en |
dc.subject | Biomedical data | en |
dc.subject | Dimensionality reduction | en |
dc.subject | Ensemble classification | en |
dc.subject | Random projections | en |
dc.subject | RNA-Seq datum | en |
dc.subject | Data reduction | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Biomedical Data Ensemble Classification using Random Projections | en |
dc.type | conferenceItem | en |
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