| 1. |
- Gamalielsson, Jonas, et al.
(författare)
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A Gene Ontology based Method for Assessing the Biological Plausibility of Regulatory Hypotheses
- 2005
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Many algorithms that derive gene regulatory networks from microarray gene expression data have been proposed in the literature. The performance of such an algorithm is often measured by how well a genetic network can recreate the gene expression data that the network was derived from. However, this kind of performance does not necessarily mean that the regulatory hypotheses in the network are biologically plausible. We therefore propose a Gene Ontology based method for assessing the biological plausibility of regulatory hypotheses at the gene product level using prior biological knowledge in the form of Gene Ontology annotation of gene products and regulatory pathway databases. Templates are designed to encode general knowledge, derived by generalizing from known interactions to typical properties of interacting gene product pairs. By matching regulatory hypotheses to templates, the plausible hypotheses can be separated from inplausible ones. In a cross-validation test we verify that the templates reliably identify interactions which have not been used in the template creation process, thereby confirming the generality of the approach. The method also proves useful when applied to an example network reconstruction problem, where a Bayesian approach is used to create hypothetical relations which are evaluated for biological plausibility. The cell cycle pathway and the MAPK signaling pathway for S. cerevisiae and H. sapiens are used in the experiments.
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| 2. |
- Gamalielsson, Jonas, et al.
(författare)
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A GO-based Method for Assessing the Biological Plausibility of Regulatory Hypotheses
- 2006
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Ingår i: Computational Science - ICCS 2006. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540343813 - 9783540343820 ; , s. 879-886
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Many algorithms have been proposed for deriving regulatory networks from microarray gene expression data. The performance of such algorithms is often measured by how well the resulting network can recreate the gene expression data that it was derived from. However, this kind of performance does not necessarily mean that the regulatory hypotheses in the network are biologically plausible. We therefore propose a method for assessing the biological plausibility of regulatory hypotheses using prior knowledge in the form of regulatory pathway databases and Gene Ontology-based annotation of gene products. A set of templates is derived by generalising from known interactions to typical properties of interacting gene product pairs. By searching for matches in this set of templates, the plausibility of regulatory hypotheses can be assessed. We evaluate to what degree the collection of templates can separate true from false positive interactions, and we illustrate the practical use of the method by applying it to an example network reconstruction problem.
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| 3. |
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| 4. |
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| 5. |
- Gamalielsson, Jonas, et al.
(författare)
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Gene Ontology-based Semantic Alignment of Biological Pathways by Evolutionary Search
- 2008
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Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific Publishing. - 0219-7200 .- 1757-6334. ; 6:4, s. 825-842
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Tidskriftsartikel (refereegranskat)abstract
- A large number of biological pathways have been elucidated recently, and there is a need for methods to analyze these pathways. One class of methods compares pathways semantically in order to discover parts that are evolutionarily conserved between species or to discover intraspecies similarities. Such methods usually require that the topologies of the pathways being compared are known, i.e. that a query pathway is being aligned to a model pathway. However, sometimes the query only consists of an unordered set of gene products. Previous methods for mapping sets of gene products onto known pathways have not been based on semantic comparison of gene products using ontologies or other abstraction hierarchies. Therefore, we here propose an approach that uses a similarity function defined in Gene Ontology (GO) terms to find semantic alignments when comparing paths in biological pathways where the nodes are gene products. A known pathway graph is used as a model, and an evolutionary algorithm (EA) is used to evolve putative paths from a set of experimentally determined gene products. The method uses a measure of GO term similarity to calculate a match score between gene products, and the fitness value of each candidate path alignment is derived from these match scores. A statistical test is used to assess the significance of evolved alignments. The performance of the method has been tested using regulatory pathways for S. cerevisiae and M. musculus.
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| 6. |
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| 7. |
- Gamalielsson, Jonas, et al.
(författare)
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GOSAP : Gene Ontology Based Semantic Alignment of Biological Pathways
- 2005
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A large number of biological pathways have been assembled in later years, and are being stored in databases. Hence, the need for methods to analyse these pathways has emerged. One class of methods compares pathways, in order to discover parts that are evolutionary conserved between species or to discover intra-species similarites. Most previous work has been focused on methods targeted at metabolic pathways utilising the EC enzyme hierarchy. Here, we propose a Gene Ontology (GO) based approach for finding semantic local alignments when comparing paths in biological pathways where the nodes are gene products. The method takes advantage of all three sub-ontologies, and uses a measure of semantic similarity to calculate a match score between gene products. Our proposed method is applicable to all types of biological pathways, where nodes are gene products, e.g. regulatory pathways, signalling pathways and metabolic enzyme-to-enzyme pathways. It would also be possible to extend the method to work with other types of nodes, as long as there is an ontology or abstraction hierarchy available for categorising the nodes. We demonstrate that the method is useful for studying protein regulatory pathways in S. cerevisiae, as well as metabolic pathways for the same organism.
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| 8. |
- Gamalielsson, Jonas, et al.
(författare)
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On the (lack of) robustness of gene expression data clustering
- 2004
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Ingår i: WSEAS Transactions on Biology and Biomedicine. - : World Scientific and Engineering Academy and Society. - 1109-9518 .- 2224-2902. ; 1:2, s. 198-204
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Tidskriftsartikel (refereegranskat)abstract
- We assess the robustness of partitional clustering algorithms applied to gene expression data. A number of clusterings are made with identical parameter settings and input data using SOM and k-means algorithms, which both rely on random initialisation and may produce different clusterings with different seeds. We define a reproducibility index and use it to assess the algorithms. The index is based on the number of pairs of genes consistently clustered together in different clusterings. The effect of noise applied to the original data is also studied. Our results show a lack of robustness for both classes of algorithms, with slightly higher reproducibility for SOM than for k-means.
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| 9. |
- Laurio, Kim, et al.
(författare)
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Evolutionary search for improved path diagrams
- 2007
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Ingår i: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540717829 - 9783540717836 ; , s. 114-121
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Konferensbidrag (refereegranskat)abstract
- A path diagram relates observed, pairwise, variable correlations to a functional structure which describes the hypothesized causal relations between the variables. Here we combine path diagrams, heuristics and evolutionary search into a system which seeks to improve existing gene regulatory models. Our evaluation shows that once a correct model has been identified it receives a lower prediction error compared to incorrect models, indicating the overall feasibility of this approach. However, with smaller samples the observed correlations gradually become more misleading, and the evolutionary search increasingly converges on suboptimal models. Future work will incorporate publicly available sources of experimentally verified biological facts to computationally suggest model modifications which might improve the model’s fitness.
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| 10. |
- Lubovac, Zelmina, et al.
(författare)
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Combining functional and topological properties to identify core modules in protein interaction networks
- 2006
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Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 64:4, s. 948-959
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Tidskriftsartikel (refereegranskat)abstract
- Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.
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