39 research outputs found
BicAT: a biclustering analysis toolbox
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 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. Contact: [email protected]
Automatic configuration of NSGA-II with jMetal and irace
jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art. Although it has become a widely used tool in the area, it lacks support for automatic tuning of algorithm parameter settings, which can prevent obtaining accurate Pareto front approximations, especially for inexperienced users. In this paper, we present a first approach to combine jMetal and irace, a package for automatic algorithm configuration; the NSGA-II is chosen as the target algorithm to be tuned. The goal is to facilitate the combined use of both tools to jMetal users to avoid wasting time in adjusting manually the parameters of the algorithms. Our proposal involves the definition of a new algorithm template for evolutionary algorithms, which allows the flexible composition of multi-objective evolutionary algorithms from a set of configurable components, as well as the generation of configuration files for adjusting the algorithm parameters with irace. To validate our approach, NSGA-II is tuned with a benchmark problems and compared with the same algorithm using standard settings, resulting in a new variant that shows a competitive behavior.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes
The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene
expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling
ability of the system architecture, multiorganism data storage and analysis capability, and availability of
computationally intensive analysis methods. 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. In addition to improved querying features, Genevestigator V3 provides new tools to analyze
the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into
expression modules, and to model expression responses in the context of metabolic and regulatory networks.
Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery
research and hypothesis validation
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana
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