| 1. |
- Costa, Ivan G, et al.
(författare)
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Inferring differentiation pathways from gene expression.
- 2008
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Ingår i: Bioinformatics (Oxford, England). - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 24:13
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Tidskriftsartikel (refereegranskat)abstract
- The regulation of proliferation and differentiation of embryonic and adult stem cells into mature cells is central to developmental biology. Gene expression measured in distinguishable developmental stages helps to elucidate underlying molecular processes. In previous work we showed that functional gene modules, which act distinctly in the course of development, can be represented by a mixture of trees. In general, the similarities in the gene expression programs of cell populations reflect the similarities in the differentiation path.We propose a novel model for gene expression profiles and an unsupervised learning method to estimate developmental similarity and infer differentiation pathways. We assess the performance of our model on simulated data and compare it with favorable results to related methods. We also infer differentiation pathways and predict functional modules in gene expression data of lymphoid development.We demonstrate for the first time how, in principal, the incorporation of structural knowledge about the dependence structure helps to reveal differentiation pathways and potentially relevant functional gene modules from microarray datasets. Our method applies in any area of developmental biology where it is possible to obtain cells of distinguishable differentiation stages.The implementation of our method (GPL license), data and additional results are available at http://algorithmics.molgen.mpg.de/Supplements/InfDif/.Supplementary data is available at Bioinformatics online.
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| 2. |
- de Souto, Marcilio C P, et al.
(författare)
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Clustering cancer gene expression data: a comparative study.
- 2008
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Ingår i: BMC bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context.We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/.
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| 3. |
- De Souto, Marcilio C P, et al.
(författare)
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Comparative study on normalization procedures for cluster analysis of gene expression datasets
- 2008
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Ingår i: Proceedings of the International Joint Conference on Neural Networks.
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Konferensbidrag (refereegranskat)abstract
- Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data. The results are presented in terms of the recovering of the true cluster structure as found by five different clustering algorithms. ©2008 IEEE.
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| 4. |
- De Souto, Marcilio C P, et al.
(författare)
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Ranking and selecting clustering algorithms using a meta-learning approach
- 2008
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Ingår i: Proceedings of the International Joint Conference on Neural Networks.
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Konferensbidrag (refereegranskat)abstract
- We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets. © 2008 IEEE.
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| 5. |
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| 6. |
- Macula, Anthony J, et al.
(författare)
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New, improved, and practical k-stem sequence similarity measures for probe design.
- 2008
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Ingår i: Journal of computational biology : a journal of computational molecular cell biology. - : Mary Ann Liebert Inc. - 1557-8666. ; 15:5, s. 525-34
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Tidskriftsartikel (refereegranskat)abstract
- We define new measures of sequence similarity for oligonucleotide probe design. These new measures incorporate the nearest neighbor k-stem motifs in their definition, but can be efficiently computed by means of a bit-vector method. They are not as computationally costly as algorithms that predict nearest neighbor hybridization potential. Our new measures for sequence similarity correlate significantly better with nearest neighbor thermodynamic predictions than either BLAST or the standard edit or insertion-deletion defined similarities already in use in many different probe design applications.
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| 7. |
- Schliep, Alexander, 1967, et al.
(författare)
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Efficient algorithms for the computational design of optimal tiling arrays.
- 2008
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Ingår i: IEEE/ACM transactions on computational biology and bioinformatics. - 1557-9964. ; 5:4, s. 557-67
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Tidskriftsartikel (refereegranskat)abstract
- The representation of a genome by oligonucleotide probes is a prerequisite for the analysis of many of its basic properties, such as transcription factor binding sites, chromosomal breakpoints, gene expression of known genes and detection of novel genes, in particular those coding for small RNAs. An ideal representation would consist of a high density set of oligonucleotides with similar melting temperatures that do not cross-hybridize with other regions of the genome and are equidistantly spaced. The implementation of such design is typically called a tiling array or genome array. We formulate the minimal cost tiling path problem for the selection of oligonucleotides from a set of candidates. Computing the selection of probes requires multi-criterion optimization, which we cast into a shortest path problem. Standard algorithms running in linear time allow us to compute globally optimal tiling paths from millions of candidate oligonucleotides on a standard desktop computer for most problem variants. The solutions to this multi-criterion optimization are spatially adaptive to the problem instance. Our formulation incorporates experimental constraints with respect to specific regions of interest and trade offs between hybridization parameters, probe quality and tiling density easily. A web application is available at http://tileomatic.org.
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