Monte Carlo inference of Gaussian networks  →read more

• C. Best, J. Apostolakis, R. Zimmer:
  A Metropolis-Hastings Algorithm for Inferring Gaussian Graphical Models of Genetic Networks, preprint, January 2007.
  [Abstract]

• C.Best
  A Monte Carlo approach to Gaussian Graphical Models for Gene Networks
  presentation, Los Alamos National Laboratory, March 21, 2005.
  [PDF]

• C.Best
  A Monte Carlo approach to Gaussian Graphical Models for Gene Networks
  presentation, BCB-Seminar, MPI for Molecular Genetics, Berlin, December 1, 2004.
  [PDF]

• C. Best, R. Zimmer, J. Apostolakis
  Self-organized soft clustering, feature selection, and network inference using Gaussian processes
  poster presentation, workshop "Complex Stochastic Systems in Biology and Medicine", Dept. of Statistics, LMU Munich, October 7-8, 2004.
  [PDF]

• C. Best:
  Gaussian Graphical Models as Statistical Systems for Gene Expression Networks
  presentation, ECCB/ISMB 2004 - SIG Bioinformatics and Statistical Physics, July 30, 2004.
  [PDF]

Function prediction using Markov random fields  →read more

• C. Best, R. Zimmer, J. Apostolakis:
  Probabilistic methods for predicting protein functions in protein-protein interaction networks
  in: German Conference on Bioinformatics 2004 (to be published), October 4-6, 2004..
  [PDF]  ·  [Presentation - PDF]  ·  arxiv.org: q-bio.MN/0503018
  Abstract:
  We discuss probabilistic methods for predicting protein functions from protein-protein interaction networks. Previous work based on Markov Randon Fields is extended and compared to a general machine-learning theoretic approach. Using actual protein interaction networks for yeast from the MIPS database and GO-SLIM function assignments, we compare the predictions of the different probabilistic methods and of a standard support vector machine. It turns out that, with the currently available networks, the simple methods based on counting frequencies perform as well as the more sophisticated approaches.

• C. Best:
  Assessing probabilistic methods for predicting protein functions from biological networks
  Presentation, PROBIO project meeting, February 12, 2004.
  [PDF]

Soft clustering and multidimensional scaling  →read more

• C. Best, R. Zimmer, J. Apostolakis:
  Self-organized soft clustering, feature selection, and network inference using Gaussian processes
  poster presentation, ISMB 2004: Intelligent Systems in Molecular Biology, Glasgow, Scotland, July 31 - August 4, 2004.
  [PDF]

• C. Best, H.-C. Hege:
  Visualizing and Identifying Conformational Ensembles in Molecular Dynamics Trajectories
  Computers in Science and Engineering 4 (3), 68 (2002).
  [PDF]
  Abstract:
  Simulating the dynamics of complex biomolecules produces trajectories comprising a large number of different configurations of the molecule. These configurations must be classified into a small number of conformational ensembles representing essential changes in the shape of the molecule. Using a conformational distance measure based on the changes in intramolecular atom distances, we show that these trajectories can be visualized efficiently by a planar map such that different conformational ensembles appear as well-separated point sets. We then use statistical cluster analysis to identify clusters that represent different conformational ensembles.