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Documents authored by Alexandrov, Theodore

Document
DOI: 10.4230/DagRep.7.12.1
Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)

Authors: Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski

Published in: Dagstuhl Reports, Volume 7, Issue 12 (2018)

Abstract
Metabolites are key players in almost all biological processes, and play various functional roles providing energy, building blocks, signaling, communication, and defense. Metabolites serve as clinical biomarkers for detecting medical conditions such as cancer; small molecule drugs account for 90% of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space. Following in the steps of high-throughput sequencing, mass spectrometry (MS) has become established as a key analytical technique for large-scale studies of complex metabolite mixtures. MS-based experiments generate datasets of increasing complexity and size. The Dagstuhl Seminar on Computational Metabolomics brought together leading experts from the experimental (analytical chemistry and biology) and the computational (computer science and bioinformatics) side, to foster the exchange of expertise needed to advance computational metabolomics. The focus was on a dynamic schedule with overview talks followed by break-out sessions, selected by the participants, covering the whole experimental-computational continuum in mass spectrometry. Particular focus in this seminar was given to imaging mass spectrometry techniques that integrate a spacial component into the analysis, ranging in scale from single cells to organs and organisms.
Cite as

Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski. Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491). In Dagstuhl Reports, Volume 7, Issue 12, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{alexandrov_et_al:DagRep.7.12.1,
  author =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  title =	{{Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)}},
  pages =	{1--17},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{12},
  editor =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.12.1},
  URN =		{urn:nbn:de:0030-drops-86740},
  doi =		{10.4230/DagRep.7.12.1},
  annote =	{Keywords: algorithms, bioinformatics, cheminformatics, computational mass spectrometry, computational metabolomics, databases, imaging mass spectrometry}
}
Document
DOI: 10.4230/OASIcs.GCB.2012.39
A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data

Authors: Ilya Chernyavsky, Theodore Alexandrov, Peter Maass, and Sergey I. Nikolenko

Published in: OASIcs, Volume 26, German Conference on Bioinformatics 2012

Abstract
We propose a new method for soft spatial segmentation of matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) data which is based on probabilistic clustering with subsequent smoothing. Clustering of spectra is done with the Latent Dirichlet Allocation (LDA) model. Then, clustering results are smoothed with a Markov random field (MRF) resulting in a soft probabilistic segmentation map. We show several extensions of the basic MRF model specifically tuned for MALDI-IMS data segmentation. We describe a highly parallel implementation of the smoothing algorithm based on GraphLab framework and show experimental results.
Cite as

Ilya Chernyavsky, Theodore Alexandrov, Peter Maass, and Sergey I. Nikolenko. A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 39-48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{chernyavsky_et_al:OASIcs.GCB.2012.39,
  author =	{Chernyavsky, Ilya and Alexandrov, Theodore and Maass, Peter and Nikolenko, Sergey I.},
  title =	{{A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data}},
  booktitle =	{German Conference on Bioinformatics 2012},
  pages =	{39--48},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-44-6},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{26},
  editor =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.39},
  URN =		{urn:nbn:de:0030-drops-37163},
  doi =		{10.4230/OASIcs.GCB.2012.39},
  annote =	{Keywords: MALDI imaging mass spectrometry, hyperspectral image segmentation, probabilistic graphical models, latent Dirichlet allocation, Markov random field}
}
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