Probabilistic modeling and machine learning in structural and systems biology

This supplement contains extended versions of a selected subset of papers presented at the workshop PMSB 2007, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, from June 17 to 18, 2006.

[1]  Korbinian Strimmer,et al.  Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process , 2007, BMC Bioinformatics.

[2]  Aki Vehtari,et al.  A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data , 2007, BMC Bioinformatics.

[3]  Heikki Mannila,et al.  Constrained hidden Markov models for population-based haplotyping , 2007, BMC Bioinformatics.

[4]  Trevor I. Dix,et al.  Comparative analysis of long DNA sequences by per element information content using different contexts , 2007, BMC Bioinformatics.

[5]  Giorgio Valentini,et al.  Model order selection for bio-molecular data clustering , 2007, BMC Bioinformatics.

[6]  Pierre Geurts,et al.  Inferring biological networks with output kernel trees , 2007, BMC Bioinformatics.

[7]  Samuel Kaski,et al.  Methods for estimating human endogenous retrovirus activities from EST databases , 2007, BMC Bioinformatics.

[8]  Taesung Park,et al.  Robust imputation method for missing values in microarray data , 2007, BMC Bioinformatics.

[9]  Raya Khanin,et al.  Bayesian model-based inference of transcription factor activity , 2007, BMC Bioinformatics.

[10]  Kathleen Marchal,et al.  Validating module network learning algorithms using simulated data , 2007, BMC Bioinformatics.

[11]  Volker Roth,et al.  Improved functional prediction of proteins by learning kernel combinations in multilabel settings , 2007, BMC Bioinformatics.