Search Results

Documents authored by Hochreiter, Sepp

Document
DOI: 10.4230/DagRep.3.5.78
Computational Methods Aiding Early-Stage Drug Design (Dagstuhl Seminar 13212)

Authors: Andreas Bender, Hinrich Göhlmann, Sepp Hochreiter, and Ziv Shkedy

Published in: Dagstuhl Reports, Volume 3, Issue 5 (2013)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 13212 "Computational Methods Aiding Early-Stage Drug Design". The aim of the seminar was to bring scientists working on various aspects of drug discovery, genomic technologies and computational science (e.g., bioinformatics, chemoinformatics, machine learning, and statistics) together to explore how high dimensional data sets created by genomic technologies can be integrated to identify functional manifestations of drug actions on living cells early in the drug discovery process.
Cite as

Andreas Bender, Hinrich Göhlmann, Sepp Hochreiter, and Ziv Shkedy. Computational Methods Aiding Early-Stage Drug Design (Dagstuhl Seminar 13212). In Dagstuhl Reports, Volume 3, Issue 5, pp. 78-94, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@Article{bender_et_al:DagRep.3.5.78,
  author =	{Bender, Andreas and G\"{o}hlmann, Hinrich and Hochreiter, Sepp and Shkedy, Ziv},
  title =	{{Computational Methods Aiding Early-Stage Drug Design (Dagstuhl Seminar 13212)}},
  pages =	{78--94},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2013},
  volume =	{3},
  number =	{5},
  editor =	{Bender, Andreas and G\"{o}hlmann, Hinrich and Hochreiter, Sepp and Shkedy, Ziv},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.3.5.78},
  URN =		{urn:nbn:de:0030-drops-41791},
  doi =		{10.4230/DagRep.3.5.78},
  annote =	{Keywords: Bioinformatics, Chemoinformatics, Machine learning, Statistics, Interdisciplinary applications}
}
Document
DOI: 10.4230/DagSemProc.09081.1
09081 Abstracts Collection – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Abstracts Collection – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{biehl_et_al:DagSemProc.09081.1,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Abstracts Collection – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.1},
  URN =		{urn:nbn:de:0030-drops-20395},
  doi =		{10.4230/DagSemProc.09081.1},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
DOI: 10.4230/DagSemProc.09081.2
09081 Summary – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
The seminar centered around different aspects of similarity-based clustering with the special focus on structures. This included theoretical foundations, new algorithms, innovative applications, and future challenges for the field.
Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Summary – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{biehl_et_al:DagSemProc.09081.2,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Summary – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.2},
  URN =		{urn:nbn:de:0030-drops-20382},
  doi =		{10.4230/DagSemProc.09081.2},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact