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The vigorous growing of resources for biological pathway information helps researchers in building mechanistic hypotheses of drug targets. There are numerous open and commercial pathway sources available for Drug Discovery Scientists, but since pathways are constructed abstractions, there is quite a lot of disagreement between the sources. This project aims at integration of pathway sources based on the overlap of member proteins and protein-protein-interactions (PPIs). The main objective is to quantify the similarity between pairs of pathways in these sources. This was done by calculating…mehr

Produktbeschreibung
The vigorous growing of resources for biological pathway information helps researchers in building mechanistic hypotheses of drug targets. There are numerous open and commercial pathway sources available for Drug Discovery Scientists, but since pathways are constructed abstractions, there is quite a lot of disagreement between the sources. This project aims at integration of pathway sources based on the overlap of member proteins and protein-protein-interactions (PPIs). The main objective is to quantify the similarity between pairs of pathways in these sources. This was done by calculating three similarity score measures i.e. the Gene Content distance, PPI distance and pathway name distance. After getting the three similarity score results, Pearsons correlation was used to identify if there was a relation between results.
Autorenporträt
2012-2013 Bioinformatician at Chalmers University of Technology in Gothenburg Sweden.2010-2012 MSc in Molecular Biology at Gothenburg University in Gothenburg, Sweden.2004-2007 B.Sc in Computer Science at Addis Ababa University in Addis Ababa, Ethiopia.