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Documents authored by Fröhlich, Holger

Document
DOI: 10.4230/DagRep.7.11.130
Addressing the Computational Challenges of Personalized Medicine (Dagstuhl Seminar 17472)

Authors: Niko Beerenwinkel, Holger Fröhlich, and Susan A. Murphy

Published in: Dagstuhl Reports, Volume 7, Issue 11 (2018)

Abstract
This report provides an overview of the talks and the working group reports from the Dagstuhl Seminar 17472 "Addressing the Computational Challenges of Personalized Medicine". The seminar brought together leading computational scientists with different backgrounds and perspectives in order to allow for a cross-fertilizing and stimulating discussion. It thus joined expertise that is usually scattered in different research communities. In addition, selected medical researchers, pharmacogenomics researchers and behavioral scientists provided their input and established the link of the computational to the more medical aspects of personalized medicine (PM). The talks and corresponding discussion spanned mainly three areas: 1) how to enhance prediction performance of computational models for PM; 2) how to improve their interpretability; 3) how to validate and implement them in practice.
Cite as

Niko Beerenwinkel, Holger Fröhlich, and Susan A. Murphy. Addressing the Computational Challenges of Personalized Medicine (Dagstuhl Seminar 17472). In Dagstuhl Reports, Volume 7, Issue 11, pp. 130-141, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{beerenwinkel_et_al:DagRep.7.11.130,
  author =	{Beerenwinkel, Niko and Fr\"{o}hlich, Holger and Murphy, Susan A.},
  title =	{{Addressing the Computational Challenges of Personalized Medicine (Dagstuhl Seminar 17472)}},
  pages =	{130--141},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{11},
  editor =	{Beerenwinkel, Niko and Fr\"{o}hlich, Holger and Murphy, Susan A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.11.130},
  URN =		{urn:nbn:de:0030-drops-86730},
  doi =		{10.4230/DagRep.7.11.130},
  annote =	{Keywords: data science, machine learning, computational modeling, bioinformatics, systems biology}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.46
Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks

Authors: Holger Fröhlich and Gunnar W. Klau

Published in: OASIcs, Volume 34, German Conference on Bioinformatics 2013

Abstract
Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies are applied to obtain edge frequencies. In a more general sense edge frequencies can also result from integrating networks learned on different datasets or via different inference algorithms. Subsequently one typically wants to derive a biological interpretation from the results in terms of a consensus network. We here propose a log odds based edge score on the basis of the expected false positive rate and thus avoid the selection of a subjective edge frequency cutoff. Computing a score optimal consensus network in our new model amounts to solving the maximum weight acyclic subdigraph problem. We use a branch-and-cut algorithm based on integer linear programming for this task. Our empirical studies on simulated and real data demonstrate a consistently improved network reconstruction accuracy compared to two threshold based strategies.
Cite as

Holger Fröhlich and Gunnar W. Klau. Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 46-55, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{frohlich_et_al:OASIcs.GCB.2013.46,
  author =	{Fr\"{o}hlich, Holger and Klau, Gunnar W.},
  title =	{{Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{46--55},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2013.46},
  URN =		{urn:nbn:de:0030-drops-42273},
  doi =		{10.4230/OASIcs.GCB.2013.46},
  annote =	{Keywords: Bayesian Networks, Network Reverse Engineering, Minimum Feedback Arc Set, Maximum Acyclic Subgraph, Molecular Interaction Networks}
}
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