| 1. |
- Kuhn, C., et al.
(författare)
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Modeling yeast osmoadaptation at different levels of resolution
- 2013
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Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific Pub Co Pte Ltd. - 0219-7200 .- 1757-6334. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e. g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.
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| 2. |
- Kühn, Clemens, et al.
(författare)
-
Modeling yeast osmoadaptation at different levels of resolution
- 2013
-
Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:2, s. 1330001-
-
Forskningsöversikt (refereegranskat)abstract
- We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e. g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.
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| 3. |
- Ma, Zhanyu, 1982-, et al.
(författare)
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A variational bayes beta mixture model for feature selection in DNA methylation studies
- 2013
-
Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:4, s. 1350005-
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Tidskriftsartikel (refereegranskat)abstract
- An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.
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