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Volume 8 Supplement 2

Probabilistic Modeling and Machine Learning in Structural and Systems Biology


Edited by Samuel Kaski, Juho Rousu, Esko Ukkonen

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

Tuusula, Finland17-18 June 2006

  1. 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...

    Authors: Simon Rogers, Raya Khanin and Mark Girolami
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S2
  2. Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low ...

    Authors: Rainer Opgen-Rhein and Korbinian Strimmer
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S3
  3. 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...

    Authors: Pierre Geurts, Nizar Touleimat, Marie Dutreix and Florence d'Alché-Buc
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S4
  4. 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...

    Authors: 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
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S5
  5. 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...

    Authors: Dankyu Yoon, Eun-Kyung Lee and Taesung Park
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S6
  6. 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...

    Authors: Alberto Bertoni and Giorgio Valentini
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S7
  7. 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...

    Authors: Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M Mäkelä, Markku J Savolainen, Minna L Hannuksela, Kimmo Kaski and Mika Ala-Korpela
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S8
  8. 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...

    Authors: Niels Landwehr, Taneli Mielikäinen, Lauri Eronen, Hannu Toivonen and Heikki Mannila
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S9
  9. Features of a DNA sequence can be found by compressing the sequence under a suitable model; good compression implies low information content. Good DNA compression models consider repetition, differences betwee...

    Authors: Trevor I Dix, David R Powell, Lloyd Allison, Julie Bernal, Samira Jaeger and Linda Stern
    Citation: BMC Bioinformatics 2007 8(Suppl 2):S10

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