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Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study

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Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study. / Ioannou, A.; Fokianos, K.; Promponas, V.J.
In: BioSystems, Vol. 100, No. 2, 05.2010, p. 132-143.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ioannou, A, Fokianos, K & Promponas, VJ 2010, 'Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study', BioSystems, vol. 100, no. 2, pp. 132-143. https://doi.org/10.1016/j.biosystems.2010.02.008

APA

Ioannou, A., Fokianos, K., & Promponas, V. J. (2010). Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study. BioSystems, 100(2), 132-143. https://doi.org/10.1016/j.biosystems.2010.02.008

Vancouver

Ioannou A, Fokianos K, Promponas VJ. Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study. BioSystems. 2010 May;100(2):132-143. Epub 2010 Mar 4. doi: 10.1016/j.biosystems.2010.02.008

Author

Ioannou, A. ; Fokianos, K. ; Promponas, V.J. / Spectral density ratio based clustering methods for the binary segmentation of protein sequences : A comparative study. In: BioSystems. 2010 ; Vol. 100, No. 2. pp. 132-143.

Bibtex

@article{77200c93a8474c188799c01a8df57158,
title = "Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study",
abstract = "We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of two or more unknown spectral density functions has a parametric linear combination of cosines. Maximum likelihood inference is worked out in detail and it is shown that its output yields several distance measures among independent stationary time series. These similarity indices are suitable for clustering time series data based on their second order properties. Other spectral domain based distances are investigated as well; and we compare all methods and distances to the problem of producing segmentations of bacterial outer membrane proteins consistent with their transmembrane topology. Protein sequences are transformed to time series data by employing numerical scales of physicochemical parameters. We also present interesting results on the prediction of transmembrane -strands, based on the clustering outcome, for a representative set of bacterial outer membrane proteins with given three-dimensional structure.",
keywords = "Distance measures, OMP topology prediction, Physicochemical parameters, Protein sequence segmentation, Spectral analysis, Periodogram, Time series",
author = "A. Ioannou and K. Fokianos and V.J. Promponas",
year = "2010",
month = may,
doi = "10.1016/j.biosystems.2010.02.008",
language = "English",
volume = "100",
pages = "132--143",
journal = "BioSystems",
issn = "0303-2647",
publisher = "Elsevier Ireland Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Spectral density ratio based clustering methods for the binary segmentation of protein sequences

T2 - A comparative study

AU - Ioannou, A.

AU - Fokianos, K.

AU - Promponas, V.J.

PY - 2010/5

Y1 - 2010/5

N2 - We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of two or more unknown spectral density functions has a parametric linear combination of cosines. Maximum likelihood inference is worked out in detail and it is shown that its output yields several distance measures among independent stationary time series. These similarity indices are suitable for clustering time series data based on their second order properties. Other spectral domain based distances are investigated as well; and we compare all methods and distances to the problem of producing segmentations of bacterial outer membrane proteins consistent with their transmembrane topology. Protein sequences are transformed to time series data by employing numerical scales of physicochemical parameters. We also present interesting results on the prediction of transmembrane -strands, based on the clustering outcome, for a representative set of bacterial outer membrane proteins with given three-dimensional structure.

AB - We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of two or more unknown spectral density functions has a parametric linear combination of cosines. Maximum likelihood inference is worked out in detail and it is shown that its output yields several distance measures among independent stationary time series. These similarity indices are suitable for clustering time series data based on their second order properties. Other spectral domain based distances are investigated as well; and we compare all methods and distances to the problem of producing segmentations of bacterial outer membrane proteins consistent with their transmembrane topology. Protein sequences are transformed to time series data by employing numerical scales of physicochemical parameters. We also present interesting results on the prediction of transmembrane -strands, based on the clustering outcome, for a representative set of bacterial outer membrane proteins with given three-dimensional structure.

KW - Distance measures

KW - OMP topology prediction

KW - Physicochemical parameters

KW - Protein sequence segmentation

KW - Spectral analysis

KW - Periodogram

KW - Time series

U2 - 10.1016/j.biosystems.2010.02.008

DO - 10.1016/j.biosystems.2010.02.008

M3 - Journal article

VL - 100

SP - 132

EP - 143

JO - BioSystems

JF - BioSystems

SN - 0303-2647

IS - 2

ER -