| 1. |
- Gamalielson, Jonas
(författare)
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Developing Semantic Pathway Alignment Algorithms for Systems Biology
- 2006
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Rapport (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 all 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 predict and understand 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. There is a lack of and need for algorithms for analysis of biological pathways. Here, a thesis is proposed where three related methods are developed for semantic analysis of biological pathways utilising the Gene Ontology. It is believed that the methods will be useful to biologists in order to assess the biological plausibility of derived pathways, compare different pathways for semantic similarities, and to derive hypothetical pathways that are semantically similar to documented biological pathways. To our knowledge, all methods are novel, and will therefore extend the bioinformatics toolbox that biologists can use to make new biological discoveries.
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| 2. |
- Gamalielson, Jonas
(författare)
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Methods for Assessing the Interestingness of Rules Induced from Microarray Gene Expression Data
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Microarray technology makes it possible to simultaneously measure the expression of thousands of genes. Gene expression data can be analysed in many different ways to produce putative knowledge on for example co-regulated genes, differentially expressed genes and how genes interact with each other. One way to derive gene interactions is to use rule induction algorithms such as association rule discovery algorithms or decision trees. The application of such algorithms to gene expression data sets typically generates a large set of rules serving as hypotheses of how genes interact. It is necessary to apply different measures to assess the interestingness of the rule hypotheses. There are well known domain independent objective measures, but there is a lack of domain specific interestingness measures tailored for microarray gene expression data. Without domain specific interestingness measures it is impossible to know if the hypotheses are interesting from a biological perspective, without resorting to time consuming manual evaluation of every single rule. The aim and contribution of this work is to develop a method for assessing the interestingness of rules induced from microarray gene expression data using a combination of objective and domain specific measures.
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| 3. |
- Gamalielson, Jonas, et al.
(författare)
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On the Robustness of Algorithms for Clustering of Gene Expression Data
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The progress in microarray technology is evident and huge amounts of gene expression data are currently being produced. A complicating matter is that there are various sources of uncertainty in microarray experiments, as well as in the analysis of expression data. This problem has generated an increased interest in the validation of methods for analysis of expression data. Clustering algorithms have been found particularly useful for the study of coexpressed genes, and this paper therefore concerns the robustness of partitional clustering algorithms. These algorithms use a predefined number of clusters and assign each gene to exactly one cluster. The effect of repeated clustering using identical algorithm parameters and input data is investigated for the self-organizing map (SOM) and the $k$-means algorithm. The susceptibility to measurement noise is also studied. A reproducibility measure is proposed and used to assess the results from the performed clustering experiments. Well-known publicly available datasets are used. Results show that clusterings are not necessarily reproducible even when identical algorithm parameters are used, and that the problems are aggravated when measurement noise is introduced.
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