S
Stefan Bleuler
Researcher at ETH Zurich
Publications - 29
Citations - 5920
Stefan Bleuler is an academic researcher from ETH Zurich. The author has contributed to research in topics: Evolutionary algorithm & Biclustering. The author has an hindex of 17, co-authored 29 publications receiving 5611 citations. Previous affiliations of Stefan Bleuler include École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes.
Tomas Hruz,Oliver Laule,Gabor Szabo,Frans Wessendorp,Stefan Bleuler,Lukas Oertle,Peter Widmayer,Wilhelm Gruissem,Philip Zimmermann +8 more
TL;DR: Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis.
Journal ArticleDOI
A systematic comparison and evaluation of biclustering methods for gene expression data
Amela Prelić,Stefan Bleuler,Philip Zimmermann,Anja Wille,Peter Bühlmann,Wilhelm Gruissem,Lars Hennig,Lothar Thiele,Eckart Zitzler +8 more
TL;DR: A methodology for comparing and validating biclustering methods that includes a simple binary reference model that captures the essential features of most bic Lustering approaches and proposes a fast divide-and-conquer algorithm (Bimax).
Book ChapterDOI
A Tutorial on Evolutionary Multiobjective Optimization
TL;DR: This work states that evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making.
Journal Article
PISA: A platform and programming language independent interface for search algorithms
TL;DR: In this article, the problem representation together with the variation operators is seen as an integral part of the optimization problem and can hence be easily separated from the selection operators, which makes it possible to specify and implement representation-independent selection modules, which form the essence of modern multiobjective optimization algorithms.
Journal ArticleDOI
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana
Anja Wille,Anja Wille,Philip Zimmermann,Philip Zimmermann,Eva Vranová,Eva Vranová,Andreas Fürholz,Andreas Fürholz,Oliver Laule,Oliver Laule,Stefan Bleuler,Stefan Bleuler,Lars Hennig,Lars Hennig,Amela Prelić,Amela Prelić,Peter von Rohr,Peter von Rohr,Lothar Thiele,Lothar Thiele,Eckart Zitzler,Eckart Zitzler,Wilhelm Gruissem,Wilhelm Gruissem,Peter Bühlmann,Peter Bühlmann +25 more
TL;DR: A novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations is presented and modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways are detected.