Title |
Sparse representation approaches for the classification of high-dimensional biological data
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Published in |
BMC Systems Biology, October 2013
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DOI | 10.1186/1752-0509-7-s4-s6 |
Pubmed ID | |
Authors |
Yifeng Li, Alioune Ngom |
Abstract |
High-throughput genomic and proteomic data have important applications in medicine including prevention, diagnosis, treatment, and prognosis of diseases, and molecular biology, for example pathway identification. Many of such applications can be formulated to classification and dimension reduction problems in machine learning. There are computationally challenging issues with regards to accurately classifying such data, and which due to dimensionality, noise and redundancy, to name a few. The principle of sparse representation has been applied to analyzing high-dimensional biological data within the frameworks of clustering, classification, and dimension reduction approaches. However, the existing sparse representation methods are inefficient. The kernel extensions are not well addressed either. Moreover, the sparse representation techniques have not been comprehensively studied yet in bioinformatics. |
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