| 1. |
- Gamalielsson, Jonas, et al.
(författare)
-
A Gene Ontology based Method for Assessing the Biological Plausibility of Regulatory Hypotheses
- 2005
-
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.
|
|
| 2. |
- Gamalielsson, Jonas, et al.
(författare)
-
A GO-based Method for Assessing the Biological Plausibility of Regulatory Hypotheses
- 2006
-
Ingår i: Computational Science - ICCS 2006. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540343813 - 9783540343820 ; , s. 879-886
-
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.
|
|
| 3. |
- Gamalielsson, Jonas
(författare)
-
Developing Semantic Pathway Comparison Methods for Systems Biology
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Systems biology is an emerging multi-disciplinary field in which the behaviour of complex biological systems is studied by considering the interaction of many cellular and molecular constituents rather than using a “traditional” reductionist approach where constituents are studied individually. Systems are often studied over time with the ultimate goal of developing models which can be used to understand and predict complex biological processes, such as human diseases. To support systems biology, a large number of biological pathways are being derived for many different organisms, and these are stored in various databases. This pathway collection presents an opportunity to compare and contrast pathways, and to utilise the knowledge they represent. This thesis presents some of the first algorithms that are designed to explore this opportunity. It is argued that the methods will be useful to biologists in order to assess the biological plausibility of derived pathways, compare different biological pathways for semantic similarities, and to derive putative pathways that are semantically similar to documented biological pathways. The methods will therefore extend the systems biology toolbox that biologists can use to make new biological discoveries.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Gamalielsson, Jonas, et al.
(författare)
-
Gene Ontology-based Semantic Alignment of Biological Pathways by Evolutionary Search
- 2008
-
Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific Publishing. - 0219-7200 .- 1757-6334. ; 6:4, s. 825-842
-
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.
|
|
| 7. |
|
|
| 8. |
- Gamalielsson, Jonas, et al.
(författare)
-
GOSAP : Gene Ontology Based Semantic Alignment of Biological Pathways
- 2005
-
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.
|
|
| 9. |
- Gamalielsson, Jonas, et al.
(författare)
-
Social Network Analysis of the Nagios Community
- 2009
-
Ingår i: Proceedings of the Open Source Workshop OSW 2009. - Skövde : University of Skövde. - 9789197851336 ; , s. 9-12
-
Konferensbidrag (refereegranskat)abstract
- The health of an Open Source ecosystem is an important decision factor when considering the adoption of Open Source software. In this paper we assess ecosystem health of the Nagios community using an approach involving analysis of social networks derived from mailing lists. Our investigation focuses on the extent to which core developers act as mediators between participants of the mailing lists.
|
|
| 10. |
- Gamalielsson, Jonas
(författare)
-
Thesis Methods : Assessing the Biological Plausibility of Regulatory Hypotheses
- 2005
-
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 have therefore proposed 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 (GO) annotation of gene products and regulatory pathway databases (Gamalielsson et al. 2005). Templates were 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. This document elaborates on how the present method can be improved and extended.
|
|
| 11. |
- Laurio, Kim, et al.
(författare)
-
Evolutionary search for improved path diagrams
- 2007
-
Ingår i: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540717829 - 9783540717836 ; , s. 114-121
-
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.
|
|
| 12. |
- Lubovac, Zelmina, et al.
(författare)
-
Combining functional and topological properties to identify core modules in protein interaction networks
- 2006
-
Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 64:4, s. 948-959
-
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.
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
- Lubovac, Zelmina, et al.
(författare)
-
Weighted Clustering Coefficient for Identifying Modular Formations in Protein-Protein Interaction Networks
- 2006
-
Ingår i: Proceedings of World Academy of Science, Engineering and Technology, Vol 14. - : World Academy of Science, Engineering and Technology. ; , s. 122-127
-
Konferensbidrag (refereegranskat)abstract
- This paper describes a novel approach for deriving modules from protein-protein interaction networks, which combines functional information with topological properties of the network. This approach is based on weighted clustering coefficient, which uses weights representing the functional similarities between the proteins. These weights are calculated according to the semantic similarity between the proteins, which is based on their Gene Ontology terms. We recently proposed an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The rational underlying this approach is that each module can be reduced to a set of triangles (protein triplets connected to each other). Here, we propose considering semantic similarity weights of all triangle-forming edges between proteins. We also apply varying semantic similarity thresholds between neighbours of each node that are not neighbours to each other (and hereby do not form a triangle), to derive new potential triangles to include in module-defining procedure. The results show an improvement of pure topological approach, in terms of number of predicted modules that match known complexes.
|
|
| 16. |
- Lubovac, Zelmina, et al.
(författare)
-
Weighted Cohesiveness for Identification of Functional Modules and their Interconnectivity
- 2007
-
Ingår i: Bioinformatics Research and Development. - Berlin, Heidelberg : Springer. - 9783540712329 - 9783540712336 ; , s. 185-198
-
Konferensbidrag (refereegranskat)abstract
- Systems biology offers a holistic perspective where individual proteins are viewed as elements in a network of protein-protein interactions (PPI), in which the proteins have contextual functions within functional modules. In order to facilitate the identification and analysis of such modules, we have previously proposed a Gene Ontology-weighted clustering coefficient for identification of modules in PPI networks and a method, named SWEMODE (Semantic WEights for MODule Elucidation), where this measure is used to identify network modules. Here, we introduce novel aspects of the method that are tested and evaluated. One of the aspects that we consider is to use the k-core graph instead of the original protein-protein interaction graph.Also, by taking the spatial aspect into account, by using the GO cellular component annotation when calculating weighted cohesiveness, we are able to improve the results compared to previous work where only two of the GO aspects (molecular function and biological process) were combined. We here evaluate the predicted modules by calculating their overlap with MIPS functional complexes. In addition, we identify the “most frequent” proteins, i.e. the proteins that most frequently participate in overlapping modules. We also investigate the role of these proteins in the interconnectivity between modules. We find that the majority of identified proteins are involved in the assembly and arrangement of cell structures, such as the cell wall and cell envelope.
|
|
| 17. |
- Lundell, Björn, et al.
(författare)
-
Exploring health within OSS ecosystems
- 2009
-
Ingår i: Proceedings of the First International Workshop on Building Sustainable Open Source Communities. - : Tampere University of Technology. - 9789521521553
-
Konferensbidrag (refereegranskat)abstract
- Open Source Software (OSS) is software which can be freely used, modified and redistributed, generally produced in a collaborative fashion by global communities of firms and individuals. In this paper we consider OSS ecosystems using an analytical device which we refer to as the OSS Stakeholder triangle. We believe that the OSS Stakeholder triangle constitues a useful means for analysing many aspects of Open Source ecosystems, including interaction between stakeholder roles and the overall health of an ecosystem.
|
|
| 18. |
|
|
| 19. |
- Synnergren, Jane, et al.
(författare)
-
A Data Integration Method for Exploring Gene Regulatory Mechanisms
- 2008
-
Ingår i: Conference on Information and Knowledge Management. - New York, NY, USA : ACM Press. - 9781605582511 ; , s. 81-84
-
Konferensbidrag (refereegranskat)abstract
- Systems biology aims to understand the behavior of and interaction between various components of the living cell, such as genes, proteins, and metabolites. A large number of components are involved in these complex systems and the diversity of relationships between the components can be overwhelming, and there is therefore a need for analysis methods incorporating data integration. We here present a method for exploring gene regulatory mechanisms which integrates various types of data to assist the identification of important components in gene regulation mechanisms. By first analyzing gene expression data, a set of differentially expressed genes is selected. These genes are then further investigated by combining various types of biological information, such as clustering results, promoter sequences, binding sites, transcription factors and other previously published information regarding the selected genes. Inspired by Information Fusion research, we also mapped functions of the proposed method to the well-known OODA-model to facilitate application of this data integration method in other research communities. We have successfully applied the method to genes identified as differentially expressed in human embryonic stem cells at different stages of differentiation towards cardiac cells. We identified 15 novel motifs that may represent important binding sites in the cardiac cell linage.
|
|
| 20. |
- Synnergren, Jane, et al.
(författare)
-
Classification of information fusion methods in systems biology
- 2009
-
Ingår i: In Silico Biology. - : IOS Press. - 1386-6338. ; 9:3, s. 65-76
-
Forskningsöversikt (refereegranskat)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.
|
|
| 21. |
- Synnergren, Jane, et al.
(författare)
-
Mapping of the JDL data fusion model to bioinformatics
- 2007
-
Ingår i: 2007 IEEE International Conference on Systems, Man and Cybernetics. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781424409914 - 9781424409907 ; , s. 1506-1511
-
Konferensbidrag (refereegranskat)abstract
- Information fusion (IF) is a rapidly developing research area which concerns the study of methods to combine the analysis of different data sources in such a way that it increases our understanding of the system under study. The synergistic effects of using multiple data sources and repeatedly updating the model when new data is available, increases the reliability of the model and makes it better suited for e.g. decision support. However, information fusion is a challenging task and more research is needed on how to best integrate data of heterogeneous types and structures in a combined analysis. Initially, IF was mainly used in military contexts, but the algorithms developed are likely to be useful in many other domains. The JDL Data Fusion Model was developed to facilitate IF processes. Here, we investigate its applicability for bioinformatics problems in general and we present an example where it is applied in a study of stem cell differentiation.
|
|
