Noise correction in gene expression data: a new approach based on subspace method
Authors: Alharbi, N., Ghodsi, Z. and Hassani, H.
Journal: Mathematical Methods in the Applied Sciences
Volume: 39
Issue: 13
Pages: 3750-3757
eISSN: 1099-1476
ISSN: 0170-4214
DOI: 10.1002/mma.3823
Abstract:We present a new approach for removing the nonspecific noise from Drosophila segmentation genes. The algorithm used for filtering here is an enhanced version of singular spectrum analysis method, which decomposes a gene profile into the sum of a signal and noise. Because the main issue in extracting signal using singular spectrum analysis procedure lies in identifying the number of eigenvalues needed for signal reconstruction, this paper seeks to explore the applicability of the new proposed method for eigenvalues identification in four different gene expression profiles. Our findings indicate that when extracting signal from different genes, for optimised signal and noise separation, different number of eigenvalues need to be chosen for each gene. Copyright © 2016 John Wiley & Sons, Ltd.
https://eprints.bournemouth.ac.uk/29718/
Source: Scopus
Noise correction in gene expression data: a new approach based on subspace method
Authors: Alharbi, N., Ghodsi, Z. and Hassani, H.
Journal: MATHEMATICAL METHODS IN THE APPLIED SCIENCES
Volume: 39
Issue: 13
Pages: 3750-3757
eISSN: 1099-1476
ISSN: 0170-4214
DOI: 10.1002/mma.3823
https://eprints.bournemouth.ac.uk/29718/
Source: Web of Science (Lite)
Noise correction in gene expression data: a new approach based on subspace method
Authors: Alharbi, N., Ghodsi, Z. and Hassani, H.
Journal: MATHEMATICAL METHODS IN THE APPLIED SCIENCES
Volume: 39
Issue: 13
Pages: 3750-3757
eISSN: 1099-1476
ISSN: 0170-4214
DOI: 10.1002/mma.3823
https://eprints.bournemouth.ac.uk/29718/
Source: Manual
Noise correction in gene expression data: a new approach based on subspace method
Authors: Alharbi, N., Ghodsi, Z. and Hassani, H.
Journal: Mathematical Methods in the Applied Sciences
Volume: 39
Issue: 13
Pages: 3750-3757
ISSN: 0170-4214
Abstract:Copyright © 2016 John Wiley & Sons, Ltd. We present a new approach for removing the nonspecific noise from Drosophila segmentation genes. The algorithm used for filtering here is an enhanced version of singular spectrum analysis method, which decomposes a gene profile into the sum of a signal and noise. Because the main issue in extracting signal using singular spectrum analysis procedure lies in identifying the number of eigenvalues needed for signal reconstruction, this paper seeks to explore the applicability of the new proposed method for eigenvalues identification in four different gene expression profiles. Our findings indicate that when extracting signal from different genes, for optimised signal and noise separation, different number of eigenvalues need to be chosen for each gene. Copyright © 2016 John Wiley & Sons, Ltd.
https://eprints.bournemouth.ac.uk/29718/
Source: BURO EPrints