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

A Bertoni

A Vehtari

D Yoon

Esko Ukkonen

Juho Rousu

M Oja

N Landwehr

P Geurts

R Opgen-Rhein

S Rogers

Samuel Kaski

T Michoel

TI Dix

V Roth

BMC Bioinformatics

English

PubMed

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BioMed CentralBMC Bioinformatics
ssOpen AcceIntroduction
Probabilistic modeling and machine learning in structural and 
systems biology
Samuel Kaski*1, Juho Rousu2 and Esko Ukkonen2
Address: 1Adaptive Informatics Research Centre and Helsinki Institute for Information Technology, Laboratory of Computer and Information 
Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland and 2Helsinki Institute for Information Technology, 
Department of Computer Science, University of Helsinki, P.O. Box 68, FI-00014 University of Helsinki, Finland
Email: Samuel Kaski* - samuel.kaski@tkk.fi; Juho Rousu - juho.rousu@cs.helsinki.fi; Esko Ukkonen - esko.ukkonen@cs.helsinki.fi
* Corresponding author    
Abstract
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.
Introduction
The workshop was designed to gather together researchers
working on the extremely timely task of integrating
advanced machine learning and computational modeling
with current biological and medical research problems.
The field is particularly active because the new high-
throughput measurement techniques and biological data-
bases require advanced modeling methods but the field
progresses so rapidly that the models need to be flexible
and relatively general-purpose. Modeling approaches on
both the systems level and in structural biology have
already become a necessary part of normal research prac-
tice. While the combination of machine learning and bio-
logical research is a particularly good match with lots of
opportunities, the work requires expertise in several areas
and hence is very challenging and needs frequent interac-
tion between researchers.
The group of researchers working in this field is normally
spread thinly in the currently abundant, partly but not
fully relevant conferences: both biological and bioinfor-
matics conferences on the one hand, and pure machine
learning conferences on the other. The aim of this work-
shop was to function as a specifically targeted forum.
The workshop started a series which will be continued as
MLSB'07, Machine Learning for Structural and Systems
Biology, June 28–29, in Evry, France.
Summary of the supplement
Selected submissions were invited based on the papers
presented in the workshop. We targeted a subset of
around ten best papers, and almost succeeded. This sup-
plement contains a reviewed selection of eleven full
papers.
Two of the papers are about modeling of the causal or
physical behavior of cellular systems. Rogers et al. [1]
introduce a full-Bayesian model of kinetics of the activity
of transcription factors in gene regulation, and Opgen-
from Probabilistic Modeling and Machine Learning in Structural and Systems Biology
Tuusula, Finland. 17–18 June 2006
Published: 3 May 2007
BMC Bioinformatics 2007, 8(Suppl 2):S1 doi:10.1186/1471-2105-8-S2-S1
<supplement> <title> <p>Probabilistic Modeling and Machine Learning in Structural and Systems Biology</p> </title> <editor>Samuel Kaski, Juho Rousu, Esko Ukkonen</editor> <note>Research</note> </supplement>
This article is available from: http://www.biomedcentral.com/1471-2105/8/S2/S1
© 2007 Kaski et al; licensee BioMed Central Ltd. 
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 2
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Rhein and Strimmer [2] use shrinkage methods to esti-
mate autoregressive processes from small samples, to infer
causal gene regulatory networks.
Biological networks are modeled in three further papers as
well. Geurts et al. [3] predict links in protein-protein inter-
action networks and enzyme networks with a new kind of
kernel-based method. Michoel et al. [4] use a synthetic
data generator to evaluate the performance of methods for
learning module networks, including a new one they
introduce.
Analysis of high-throughput data is a common subtheme
in most works. Three of the papers are particularly focused
in this task. Yoon et al. [5] introduce a robust preprocess-
ing method for treating missing values in gene expression
data, and Bertoni and Valentini [6] decide the number of
clusters based on stability against fluctuations caused by
random projections. In the only paper on metabonomics,
Vehtari et al. [7] introduce a full-Bayesian way of mode-
ling the mapping between NMR spectra and clinical vari-
ables. Three of the papers are related to genomics.
Landwehr et al. [8] introduce a hidden Markov model-
based method for haplotype reconstruction which is a
subproblem of gene association studies for uncovering
genetic bases of diseases. Dix et al. [9] use compression
methods to analyze information content of DNA in a
genome-wide scale. Oja et al. [10] use hidden Markov
model-based methods to estimate activities of retroviruses
residing in human genome, using EST databases.
Finally, Roth and Fischer [11] introduce a kernel-based
fusion from multiple data sources for predicting multi-
label protein function.
Acknowledgements
We wish to thank particularly warmly the programme committee of the 
PMSB 2007 conference; many of them participated in refereeing the papers 
of this supplement as well as the original workshop papers. The programme 
committee consisted of Florence d'Alché-Buc, Université d'Evry-Val 
d'Essonne; Jaakko Astola, Tampere University of Technology; Nello Cris-
tianini, UC Davis/University of Bristol; Liisa Holm, University of Helsinki; 
Mark Girolami, University of Glasgow; Samuel Kaski, Helsinki University of 
Technology; Matej Orešič, Technical Research Centre of Finland; Juho 
Rousu, University of Helsinki; Esko Ukkonen, Helsinki Institute for Informa-
tion Technology; and Jean-Philippe Vert, Ecole des Mines de Paris. We had 
additionally several other referees; they will be acknowledged according to 
the normal practices of BMC Bioinformatics to ensure their anonymity.
We thank for financial support the EU FP6 Network of Excellence PASCAL 
(IST-2002-506778), and Helsinki Institute for Information Technology, and 
both University of Helsinki and Helsinki University of Technology for pro-
viding the infrastructure for the workshop, and finally, all contributors and 
participants for the successful workshop.
This article has been published as part of BMC Bioinformatics Volume 8, Sup-
plement 2, 2007: Probabilistic Modeling and Machine Learning in Structural 
and Systems Biology. The full contents of the supplement are available 
online at http://www.biomedcentral.com/1471-2105/8?issue=S2.
References
1. Rogers S, Khanin R, Girolami M: Bayesian model-based inference
of transcription factor activity.  BMC Bioinformatics 2007,
8(Suppl 3):S2.
2. Opgen-Rhein R, Strimmer K: Learning causal networks from sys-
tems biology time course data: an effective model selection
procedure for the vector autoregressive process.  BMC Bioin-
formatics 2007, 8(Suppl 3):S3.
3. Geurts P, Touleimat N, Dutreix M, d'Alché-Buc F: Inferring biolog-
ical networks with output kernel trees.  BMC Bioinformatics
2007, 8(Suppl 3):S4.
4. Michoel T, Maere S, Bonnet E, Joshi A, Saeys Y, Van den Bulcke T, Van
Leemput K, van Remortel P, Kuiper M, Marchal K, Van de Peer Y:
Validating module network learning algorithms using simu-
lated data.  BMC Bioinformatics 2007, 8(Suppl 3):S5.
5. Yoon D, Lee EK, Park T: Robust imputation method for missing
values in microarray data.  BMC Bioinformatics 2007, 8(Suppl
3):S6.
6. Bertoni A, Valentini G: Model order selection for bio-molecular
data clustering.  BMC Bioinformatics 2007, 8(Suppl 3):S7.
7. Vehtari A, Mäkinen VP, Soininen P, Ingman P, Mäkelä SM, Savolainen
MJ, Hannuksela ML, Kaski K, Ala-Korpela M: A novel Bayesian
approach to quantify clinical variables and to determine
their spectroscopic counterparts in 1H NMR metabonomic
data.  BMC Bioinformatics 2007, 8(Suppl 3):S8.
8. Landwehr N, Mielikäinen T, Eronen L, Toivonen H, Mannila H: Con-
strained Hidden Markov Models for population-based haplo-
typing.  BMC Bioinformatics 2007, 8(Suppl 3):S9.
9. Dix TI, Powell DR, Allison L, Bernal J, Jaeger S, Stern L: Compara-
tive analysis of long DNA sequences by per element informa-
tion content using different contexts.  BMC Bioinformatics 2007,
8(Suppl 3):S10.
10. Oja M, Peltonen J, Blomberg J, Kaski S: Methods for estimating
human endogenous retrovirus activities from EST data-
bases.  BMC Bioinformatics 2007, 8(Suppl 3):S11.
11. Roth V, Fisher B: Improved functional prediction of proteins by
learning kernel combinations in multilabel settings.  BMC Bio-
informatics 2007, 8(Suppl 3):S12.Page 2 of 2
(page number not for citation purposes)


Probabilistic modeling and machine learning in structural and systems biology

Crossref

Ala-Korpela M: A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data. BMC Bioinformatics 2007, 8(Suppl 3):S8.

Bayesian model-based inference of transcription factor activity.

d'Alché-Buc F: Inferring biological networks with output kernel trees.

de Peer Y: Validating module network learning algorithms using simulated data. BMC Bioinformatics 2007, 8(Suppl 3):S5.

Improved functional prediction of proteins by learning kernel combinations in multilabel settings.

L: Comparative analysis of long DNA sequences by per element information content using different contexts. BMC Bioinformatics 2007, 8(Suppl 3):S10.

Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 2007, 8(Suppl 3):S3.

Mannila H: Constrained Hidden Markov Models for population-based haplotyping. BMC Bioinformatics 2007, 8(Suppl 3):S9.

Model order selection for bio-molecular data clustering.

Robust imputation method for missing values in microarray data.

S: Methods for estimating human endogenous retrovirus activities from EST databases. BMC Bioinformatics 2007, 8(Suppl 3):S11.

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