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  1. Article: BicAT: a biclustering analysis toolbox.

    Barkow, Simon / Bleuler, Stefan / Prelic, Amela / Zimmermann, Philip / Zitzler, Eckart

    Bioinformatics (Oxford, England)

    2006  Volume 22, Issue 10, Page(s) 1282–1283

    Abstract: Summary: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software ...

    Abstract Summary: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments.
    Availability: The BicAT toolbox is freely available at http://www.tik.ee.ethz.ch/sop/bicat and runs on all operating systems. The Java source code of the program and a developer's guide is provided on the website as well. Therefore, users may modify the program and add further algorithms or extensions.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Cluster Analysis ; Database Management Systems ; Databases, Protein ; Gene Expression Profiling/methods ; Information Storage and Retrieval/methods ; Oligonucleotide Array Sequence Analysis/methods ; Pattern Recognition, Automated/methods ; Software ; User-Computer Interface
    Language English
    Publishing date 2006-05-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4803
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btl099
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A systematic comparison and evaluation of biclustering methods for gene expression data.

    Prelić, Amela / Bleuler, Stefan / Zimmermann, Philip / Wille, Anja / Bühlmann, Peter / Gruissem, Wilhelm / Hennig, Lars / Thiele, Lothar / Zitzler, Eckart

    Bioinformatics (Oxford, England)

    2006  Volume 22, Issue 9, Page(s) 1122–1129

    Abstract: Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often ... ...

    Abstract Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available.
    Results: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Cluster Analysis ; Databases, Genetic ; Gene Expression/physiology ; Gene Expression Profiling/methods ; Oligonucleotide Array Sequence Analysis/methods ; Pattern Recognition, Automated/methods
    Language English
    Publishing date 2006-05-01
    Publishing country England
    Document type Comparative Study ; Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't ; Validation Studies
    ZDB-ID 1422668-6
    ISSN 1367-4803
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btl060
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

    Wille, Anja / Zimmermann, Philip / Vranovà-Milcakova, Eva / Fürholz, Andreas / Laule, Oliver / Bleuler, Stefan / Hennig, Lars / Prelić, Amela / Rohr, Peter <> Thiele, Lothar / Zitzler, Eckart / Gruissem, Wilhelm / Bühlmann, Peter Lukas

    2004  

    Keywords ARABIDOPSIS (BOTANIK) ; ISOPRENOIDBIOSYNTHESE + ISOPRENOIDANABOLISMUS (METABOLISMUS) ; GENREGULATION ; REGULATION DER GENEXPRESSION (MOLEKULARBIOLOGIE) ; MODELLRECHNUNG UND SIMULATION IN DER GENETIK
    Language English
    Publisher [S.l.], BioMed Central
    Publishing country ch
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana.

    Wille, Anja / Zimmermann, Philip / Vranová, Eva / Fürholz, Andreas / Laule, Oliver / Bleuler, Stefan / Hennig, Lars / Prelic, Amela / von Rohr, Peter / Thiele, Lothar / Zitzler, Eckart / Gruissem, Wilhelm / Bühlmann, Peter

    Genome biology

    2004  Volume 5, Issue 11, Page(s) R92

    Abstract: We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, ... ...

    Abstract We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.
    MeSH term(s) Arabidopsis/genetics ; Computer Graphics/statistics & numerical data ; Computer Simulation/statistics & numerical data ; Galactose/metabolism ; Genes, Fungal/genetics ; Genes, Plant/genetics ; Genes, Plant/physiology ; Models, Genetic ; Normal Distribution ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism ; Terpenes/metabolism
    Chemical Substances Terpenes ; Galactose (X2RN3Q8DNE)
    Language English
    Publishing date 2004
    Publishing country England
    Document type Comparative Study ; Journal Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6914 ; 1465-6906
    ISSN (online) 1474-760X ; 1465-6914
    ISSN 1465-6906
    DOI 10.1186/gb-2004-5-11-r92
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

    Wille, Anja / Zimmermann, Philip / Vranovà-Milcakova, Eva / Fürholz, Andreas / Laule, Oliver / Bleuler, Stefan / Hennig, Lars / Prelić, Amela / Rohr, Peter <> Thiele, Lothar / Zitzler, Eckart / Gruissem, Wilhelm / Bühlmann, Peter Lukas

    2004  

    Keywords ARABIDOPSIS (BOTANIK); ISOPRENOIDBIOSYNTHESE + ISOPRENOIDANABOLISMUS (METABOLISMUS); GENREGULATION ; REGULATION DER GENEXPRESSION (MOLEKULARBIOLOGIE); MODELLRECHNUNG UND SIMULATION IN DER GENETIK; ARABIDOPSIS (BOTANY); ISOPRENOID BIOSYNTHESIS + ISOPRENOID ANABOLISM (METABOLISM); GENE REGULATION ; REGULATION OF GENE-EXPRESSION (MOLECULAR BIOLOGY); MATHEMATICAL MODELING AND SIMULATION IN GENETICS ; info:eu-repo/classification/ddc/570 ; Life sciences
    Language English
    Publisher BioMed Central
    Publishing country ch
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

    Wille, Anja / Bleuler, Stefan / Bühlmann, Peter / Fürholz, Andreas / Gruissem, Wilhelm / Hennig, Lars / Laule, Oliver / Prelić, Amela / Thiele, Lothar / von Rohr, Peter / Vranová, Eva / Zimmermann, Philip / Zitzler, Eckart

    Genome biology. 2004 Oct., v. 5, no. 11

    2004  

    Abstract: We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, ... ...

    Abstract We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.
    Keywords Arabidopsis thaliana ; biosynthesis ; galactose ; gene regulatory networks ; genes ; isoprenoids ; models ; yeasts
    Language English
    Dates of publication 2004-10
    Size p. R92.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6914 ; 1465-6906
    ISSN (online) 1474-760X ; 1465-6914
    ISSN 1465-6906
    DOI 10.1186/gb-2004-5-11-r92
    Database NAL-Catalogue (AGRICOLA)

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  7. Article ; Online: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

    Wille, Anja / Zimmermann, Philip / Vranová, Eva / Fürholz, Andreas / Laule, Oliver / Bleuler, Stefan / Hennig, Lars / Prelić, Amela / von Rohr, Peter / Thiele, Lothar / Zitzler, Eckart / Gruissem, Wilhelm / id_orcid:0 000-0002-1872-2998 / Bühlmann, Peter

    Genome Biology, 5 (11)

    2004  

    Abstract: We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, ... ...

    Abstract We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

    ISSN:1474-760X
    Keywords Carotenoid ; Gene Pair ; Additional Data File ; Isoprenoid ; Plastoquinone ; info:eu-repo/classification/ddc/570 ; Life sciences
    Language English
    Publishing date 2004-10-25
    Publisher BioMed Central
    Publishing country ch
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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