Volume 8 Supplement 2

Probabilistic Modeling and Machine Learning in Structural and Systems Biology

Research

Edited by Samuel Kaski, Juho Rousu, Esko Ukkonen

Probabilistic Modeling and Machine Learning in Structural and Systems Biology. Go to conference site.

Tuusula, Finland

17-18 June 2006

  1. Research

    Bayesian model-based inference of transcription factor activity

    In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for t...

    Simon Rogers, Raya Khanin and Mark Girolami

    BMC Bioinformatics 2007 8(Suppl 2):S2

    Published on: 3 May 2007

  2. Research

    Inferring biological networks with output kernel trees

    Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughpu...

    Pierre Geurts, Nizar Touleimat, Marie Dutreix and Florence d'Alché-Buc

    BMC Bioinformatics 2007 8(Suppl 2):S4

    Published on: 3 May 2007

  3. Research

    Validating module network learning algorithms using simulated data

    In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithm...

    Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van den Bulcke, Koenraad Van Leemput, Piet van Remortel, Martin Kuiper, Kathleen Marchal and Yves Van de Peer

    BMC Bioinformatics 2007 8(Suppl 2):S5

    Published on: 3 May 2007

  4. Research

    Robust imputation method for missing values in microarray data

    When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis cannot be applied when the data have missing va...

    Dankyu Yoon, Eun-Kyung Lee and Taesung Park

    BMC Bioinformatics 2007 8(Suppl 2):S6

    Published on: 3 May 2007

  5. Research

    Model order selection for bio-molecular data clustering

    Cluster analysis has been widely applied for investigating structure in bio-molecular data. A drawback of most clustering algorithms is that they cannot automatically detect the "natural" number of clusters un...

    Alberto Bertoni and Giorgio Valentini

    BMC Bioinformatics 2007 8(Suppl 2):S7

    Published on: 3 May 2007

  6. Research

    A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

    A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on c...

    Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M Mäkelä, Markku J Savolainen, Minna L Hannuksela, Kimmo Kaski and Mika Ala-Korpela

    BMC Bioinformatics 2007 8(Suppl 2):S8

    Published on: 3 May 2007

  7. Research

    Constrained hidden Markov models for population-based haplotyping

    Haplotype Reconstruction is the problem of resolving the hidden phase information in genotype data obtained from laboratory measurements. Solving this problem is an important intermediate step in gene association...

    Niels Landwehr, Taneli Mielikäinen, Lauri Eronen, Hannu Toivonen and Heikki Mannila

    BMC Bioinformatics 2007 8(Suppl 2):S9

    Published on: 3 May 2007