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Documents authored by Pfeifer, Nico

Document
DOI: 10.4230/LIPIcs.WABI.2017.16
All Fingers Are Not the Same: Handling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels

Authors: Sarvesh Nikumbh, Peter Ebert, and Nico Pfeifer

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
Most string kernels for comparison of genomic sequences are generally tied to using (absolute) positional information of the features in the individual sequences. This poses limitations when comparing variable-length sequences using such string kernels. For example, profiling chromatin interactions by 3C-based experiments results in variable-length genomic sequences (restriction fragments). Here, exact position-wise occurrence of signals in sequences may not be as important as in the scenario of analysis of the promoter sequences, that typically have a transcription start site as reference. Existing position-aware string kernels have been shown to be useful for the latter scenario. In this work, we propose a novel approach for sequence comparison that enables larger positional freedom than most of the existing approaches, can identify a possibly dispersed set of features in comparing variable-length sequences, and can handle both the aforementioned scenarios. Our approach, \emph{CoMIK}, identifies not just the features useful towards classification but also their locations in the variable-length sequences, as evidenced by the results of three binary classification experiments, aided by recently introduced visualization techniques. Furthermore, we show that we are able to efficiently retrieve and interpret the weight vector for the complex setting of multiple multi-instance kernels.
Cite as

Sarvesh Nikumbh, Peter Ebert, and Nico Pfeifer. All Fingers Are Not the Same: Handling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 16:1-16:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{nikumbh_et_al:LIPIcs.WABI.2017.16,
  author =	{Nikumbh, Sarvesh and Ebert, Peter and Pfeifer, Nico},
  title =	{{All Fingers Are Not the Same: Handling Variable-Length Sequences in a Discriminative Setting Using Conformal Multi-Instance Kernels}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{16:1--16:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.16},
  URN =		{urn:nbn:de:0030-drops-76459},
  doi =		{10.4230/LIPIcs.WABI.2017.16},
  annote =	{Keywords: Multiple instance learning, conformal MI kernels, 5C, Hi-C}
}
Document
DOI: 10.4230/DagSemProc.05471.13
OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

Authors: Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer

Published in: Dagstuhl Seminar Proceedings, Volume 5471, Computational Proteomics (2006)

Abstract
In the talk we describe the freely available software library OpenMS which is currently under development at the Freie Universität Berlin and the Eberhardt-Karls Universität Tübingen. We give an overview of the goals and problems in differential proteomics with HPLC and then describe in detail the implemented approaches for signal processing, peak detection and data reduction currently employed in OpenMS. After this we describe methods to identify the differential expression of peptides and propose strategies to avoid MS/MS identification of peptides of interest. We give an overview of the capabilities and design principles of OpenMS and demonstrate its ease of use. Finally we describe projects in which OpenMS will be or was already deployed and thereby demonstrate its versatility.
Cite as

Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer. OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{reinert_et_al:DagSemProc.05471.13,
  author =	{Reinert, Knut and Kohlbacher, Oliver and Gr\"{o}pl, Clemens and Lange, Eva and Schulz-Trieglaff, Ole and Sturm, Marc and Pfeifer, Nico},
  title =	{{OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.13},
  URN =		{urn:nbn:de:0030-drops-5463},
  doi =		{10.4230/DagSemProc.05471.13},
  annote =	{Keywords: Proteomics, C++, Differential expression}
}
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