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Documents authored by Leha, Andreas

Document
Complete Volume
DOI: 10.4230/OASIcs.GCB.2013
OASIcs, Volume 34, GCB'13, Complete Volume

Authors: Tim Beißbarth, Martin Kollmar, Andreas Leha, Burkhard Morgenstern, Anne-Kathrin Schultz, Stephan Waack, and Edgar Wingender

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

Abstract
OASIcs, Volume 34, GCB'13, Complete Volume
Cite as

German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@Proceedings{beibarth_et_al:OASIcs.GCB.2013,
  title =	{{OASIcs, Volume 34, GCB'13, Complete Volume}},
  booktitle =	{German Conference on Bioinformatics 2013},
  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},
  URN =		{urn:nbn:de:0030-drops-42563},
  doi =		{10.4230/OASIcs.GCB.2013},
  annote =	{Keywords: Life and Medical Sciences}
}
Document
Front Matter
DOI: 10.4230/OASIcs.GCB.2013.i
Frontmatter, Table of Contents, Preface, Conference Organization

Authors: Tim Beißbarth, Martin Kollmar, Andreas Leha, Burkhard Morgenstern, Anne-Kathrin Schultz, Stephan Waack, and Edgar Wingender

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

Abstract
Frontmatter, Table of Contents, Preface, Conference Organization
Cite as

German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. i-xiii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{beibarth_et_al:OASIcs.GCB.2013.i,
  author =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  title =	{{Frontmatter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{i--xiii},
  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.i},
  URN =		{urn:nbn:de:0030-drops-42265},
  doi =		{10.4230/OASIcs.GCB.2013.i},
  annote =	{Keywords: Frontmatter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.90
Utilization of ordinal response structures in classification with high-dimensional expression data

Authors: Andreas Leha, Klaus Jung, and Tim Beißbarth

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

Abstract
Molecular diagnosis or prediction of clinical treatment outcome based on high-throughput genomics data is a modern application of machine learning techniques for clinical problems. In practice, clinical parameters, such as patient health status or toxic reaction to therapy, are often measured on an ordinal scale (e.g. good, fair, poor). Commonly, the prediction of ordinal end-points is treated as a multi-class classification problem, disregarding the ordering information contained in the response. This may result in a loss of prediction accuracy. Classical approaches to model ordinal response directly, including for instance the cumulative logit model, are typically not applicable to high-dimensional data. We present hierarchical twoing (hi2), a novel algorithm for classification of high-dimensional data into ordered categories. hi2 combines the power of well-understood binary classification with ordinal response prediction. A comparison of several approaches for ordinal classification on real world data as well as simulated data shows that classification algorithms especially designed to handle ordered categories fail to improve upon state-of-the-art non-ordinal classification algorithms. In general, the classification performance of an algorithm is dominated by its ability to deal with the high-dimensionality of the data. Only hi2 outperforms its competitors in the case of moderate effects.
Cite as

Andreas Leha, Klaus Jung, and Tim Beißbarth. Utilization of ordinal response structures in classification with high-dimensional expression data. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 90-100, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{leha_et_al:OASIcs.GCB.2013.90,
  author =	{Leha, Andreas and Jung, Klaus and Bei{\ss}barth, Tim},
  title =	{{Utilization of ordinal response structures in classification with high-dimensional expression data}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{90--100},
  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.2013.90},
  URN =		{urn:nbn:de:0030-drops-42340},
  doi =		{10.4230/OASIcs.GCB.2013.90},
  annote =	{Keywords: Classification, High-Dimensional Data, Ordinal Response, Expression Data}
}
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