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Documents authored by Albrecht, Mario

Document
DOI: 10.4230/DagSemProc.08191.6
08191 Working Group Summary – Visually Comparing a Set of Graphs

Authors: Mario Albrecht, Alejandro Estrella-Balderrama, Markus Geyer, Carsten Gutwenger, Karsten Klein, Oliver Kohlbacher, and Michael Schulz

Published in: Dagstuhl Seminar Proceedings, Volume 8191, Graph Drawing with Applications to Bioinformatics and Social Sciences (2008)

Abstract
We consider methods to visually compare graphs, more to focus on the differences of the graphs than on the similarities. Our two-level approach constructs a meaningful overview of the given graphs combined with a detailed view focusing on a local area of change. The actual layout of these graphs has to be evaluated depending on the specific type of biological network to be visualized in each case. We look into different variants and propose properties to be optimized in our visualizations.
Cite as

Mario Albrecht, Alejandro Estrella-Balderrama, Markus Geyer, Carsten Gutwenger, Karsten Klein, Oliver Kohlbacher, and Michael Schulz. 08191 Working Group Summary – Visually Comparing a Set of Graphs. In Graph Drawing with Applications to Bioinformatics and Social Sciences. Dagstuhl Seminar Proceedings, Volume 8191, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{albrecht_et_al:DagSemProc.08191.6,
  author =	{Albrecht, Mario and Estrella-Balderrama, Alejandro and Geyer, Markus and Gutwenger, Carsten and Klein, Karsten and Kohlbacher, Oliver and Schulz, Michael},
  title =	{{08191 Working Group Summary – Visually Comparing a Set of Graphs}},
  booktitle =	{Graph Drawing with Applications to Bioinformatics and Social Sciences},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8191},
  editor =	{Stephen P. Borgatti and Stephen Kobourov and Oliver Kohlbacher and Petra Mutzel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08191.6},
  URN =		{urn:nbn:de:0030-drops-15536},
  doi =		{10.4230/DagSemProc.08191.6},
  annote =	{Keywords: Graph drawing, visual graph comparison}
}
Document
DOI: 10.4230/DagSemProc.08131.2
Applications of semantic similarity measures

Authors: Andreas Schlicker, Fidel Ramírez, Jörg Rahnenführer, Carola Huthmacher, Alejandro Pironti, Francisco S. Domingues, Thomas Lengauer, and Mario Albrecht

Published in: Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008)

Abstract
There has been much interest in uncovering protein-protein interactions and their underlying domain-domain interactions. Many experimental techniques have been developed, for example yeast-two-hybrid screening and tandem affinity purification. Since it is time consuming and expensive to perform exhaustive experimental screens, in silico methods are used for predicting interactions. However, all experimental and computational methods have considerable false positive and false negative rates. Therefore, it is necessary to validate experimentally determined and predicted interactions. One possibility for the validation of interactions is the comparison of the functions of the proteins or domains. Gene Ontology (GO) is widely accepted as a standard vocabulary for functional terms, and is used for annotating proteins and protein families with biological processes and their molecular functions. This annotation can be used for a functional comparison of interacting proteins or domains using semantic similarity measures. Another application of semantic similarity measures is the prioritization of disease genes. It is know that functionally similar proteins are often involved in the same or similar diseases. Therefore, functional similarity is used for predicting disease associations of proteins. In the first part of my talk, I will introduce some semantic and functional similarity measures that can be used for comparison of GO terms and proteins or protein families. Then, I will show their application for determining a confidence threshold for domain-domain interaction predictions. Additionally, I will present FunSimMat (http://www.funsimmat.de/), a comprehensive resource of functional similarity values available on the web. In the last part, I will introduce the problem of comparing diseases, and a first attempt to apply functional similarity measures based on GO to this problem.
Cite as

Andreas Schlicker, Fidel Ramírez, Jörg Rahnenführer, Carola Huthmacher, Alejandro Pironti, Francisco S. Domingues, Thomas Lengauer, and Mario Albrecht. Applications of semantic similarity measures. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{schlicker_et_al:DagSemProc.08131.2,
  author =	{Schlicker, Andreas and Ram{\'\i}rez, Fidel and Rahnenf\"{u}hrer, J\"{o}rg and Huthmacher, Carola and Pironti, Alejandro and Domingues, Francisco S. and Lengauer, Thomas and Albrecht, Mario},
  title =	{{Applications of semantic similarity measures}},
  booktitle =	{Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8131},
  editor =	{Michael Ashburner and Ulf Leser and Dietrich Rebholz-Schuhmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.2},
  URN =		{urn:nbn:de:0030-drops-15198},
  doi =		{10.4230/DagSemProc.08131.2},
  annote =	{Keywords: Semantic similarity, functional similarity, Gene Ontology, domain-domain interactions}
}
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