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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.

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

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

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.