| 1. |
- Lindlöf, Angelica, et al.
(author)
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Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks
- 2005
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In: In Silico Biology. - : IOS Press. - 1386-6338. ; 5:3, s. 239-249
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Journal article (peer-reviewed)abstract
- Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation.
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| 2. |
- Ameur, Adam, et al.
(author)
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Global gene expression analysis by combinatorial optimization
- 2004
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In: In Silico Biology. - 1386-6338. ; 4
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Journal article (peer-reviewed)abstract
- Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.
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| 3. |
- Andrade, Jorge, et al.
(author)
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Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses
- 2006
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In: In Silico Biology. - 1386-6338. ; 6:6, s. 495-504
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Journal article (peer-reviewed)abstract
- For several applications and algorithms used in applied bioinformatics, a bottle neck in terms of computational time may arise when scaled up to facilitate analyses of large datasets and databases. Re-codification, algorithm modification or sacrifices in sensitivity and accuracy may be necessary to accommodate for limited computational capacity of single work stations. Grid computing offers an alternative model for solving massive computational problems by parallel execution of existing algorithms and software implementations. We present the implementation of a Grid-aware model for solving computationally intensive bioinformatic analyses exemplified by a blastp sliding window algorithm for whole proteome sequence similarity analysis, and evaluate the performance in comparison with a local cluster and a single workstation. Our strategy involves temporary installations of the BLAST executable and databases on remote nodes at submission, accommodating for dynamic Grid environments as it avoids the need of predefined runtime environments (preinstalled software and databases at specific Grid-nodes). Importantly, the implementation is generic where the BLAST executable can be replaced by other software tools to facilitate analyses suitable for parallelisation. This model should be of general interest in applied bioinformatics. Scripts and procedures are freely available from the authors.
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| 4. |
- Gennemark, Peter, 1974, et al.
(author)
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A simple mathematical model of adaptation to high osmolarity in yeast
- 2006
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In: In silico biology. - 1434-3207 .- 1386-6338. ; 6:0018
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Journal article (peer-reviewed)abstract
- We present a simple ordinary differential equation (ODE) model of the adaptive response to an osmotic shock in the yeast Saccharomyces cerevisiae. The model consists of two main components. First, a biophysical model describing how the cell volume and the turgor pressure are affected by varying extra-cellular osmolarity. The second component describes how the cell controls the biophysical system in order to keep turgor pressure, or equivalently volume, constant. This is done by adjusting the glycerol production and the glycerol outflow from the cell. The complete model consists of 4 ODEs, 3 algebraic equations and 10 parameters. The parameters are constrained from various literature sources and estimated from new and previously published absolute time series data on intra-cellular and total glycerol. The qualitative behaviour of the model has been successfully tested on data from other genetically modified strains as well as data for different input signals. Compared to a previous detailed model of osmoregulation, the main strength of our model is its lower complexity, contributing to a better understanding of osmoregulation by focusing on relationships which are obscured in the more detailed model. Besides, the low complexity makes it possible to obtain more reliable parameter estimates.
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| 5. |
- Synnergren, Jane, et al.
(author)
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Classification of information fusion methods in systems biology
- 2009
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In: In Silico Biology. - : IOS Press. - 1386-6338. ; 9:3, s. 65-76
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Research review (peer-reviewed)abstract
- Biological systems are extremely complex and often involve thousands of interacting components. Despite all efforts, many complex biological systems are still poorly understood. However, over the past few years high-throughput technologies have generated large amounts of biological data, now requiring advanced bioinformatic algorithms for interpretation into valuable biological information. Due to these high-throughput technologies, the study of biological systems has evolved from focusing on single components (e.g. genes) to encompassing large sets of components (e.g. all genes in an entire genome), with the aim to elucidate their interdependences in various biological processes. In addition, there is also an increasing need for integrative analysis, where knowledge about the biological system is derived by data fusion, using heterogeneous data sets as input. We here review representative examples of bioinformatic methods for fusion-oriented interpretation of multiple heterogeneous biological data, and propose a classification into three categories of tasks that they address: data extraction, data integration and data fusion. The aim of this classification is to facilitate the exchange of methods between systems biology and other information fusion application areas.
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