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Biomedical subjects

Frank De Smet

Publications and source records attributed to Frank De Smet.

9 recordsLinked to original sources
PubMed2005

M@CBETH: a microarray classification benchmarking tool.

Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification.

Algorithms↗
PubMed2005

Comparison of the PhoPQ regulon in Escherichia coli and Salmonella typhimurium.

The PhoPQ two-component system acts as a transcriptional regulator that responds to Mg(2+) starvation both in Escherichia coli and Salmonella typhimurium (Garcia et al. 1996; Kato et al. 1999). By monitoring the availability of extracellular Mg(2+), this two-component system allows S. typhimurium to sense the transition from an extracellular environment to a subcellular location. Concomitantly with this transition, a set of virulence factors essential for survival in the intracellular environment is activated by the PhoPQ system (Groisman et al. 1989; Miller et al. 1989). Compared to nonpathogenic strains, such as E. coli K12, the PhoPQ regulon in pathogens must contain target genes specifically contributing to the virulence phenotype. To verify this hypothesis, we compared the composition of the PhoPQ regulon between E. coli and S. typhimurium using a combination of expression experiments and motif data. PhoPQ-dependent genes in both organisms were identified from PhoPQ-related microarray experiments. To distinguish between direct and indirect targets, we searched for the presence of the regulatory motif in the promoter region of the identified PhoPQ-dependent genes. This allowed us to reconstruct the direct PhoPQ-dependent regulons in E. coli K12 and S. typhimurium LT2. Comparison of both regulons revealed a very limited overlap of PhoPQ-dependent genes between both organisms. These results suggest that the PhoPQ system has acquired a specialized function during evolution in each of these closely related species that allows adaptation to the specificities of their lifestyles (e.g., pathogenesis in S. typhimurium).

Bacterial Proteins↗
PubMed2004

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction.

MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. The aim of this paper is to systematically benchmark the role of non-linear versus linear techniques and dimensionality reduction methods. RESULTS: A systematic benchmarking study is performed by comparing linear versions of standard classification and dimensionality reduction techniques with their non-linear versions based on non-linear kernel functions with a radial basis function (RBF) kernel. A total of 9 binary cancer classification problems, derived from 7 publicly available microarray datasets, and 20 randomizations of each problem are examined. CONCLUSIONS: Three main conclusions can be formulated based on the performances on independent test sets. (1) When performing classification with least squares support vector machines (LS-SVMs) (without dimensionality reduction), RBF kernels can be used without risking too much overfitting. The results obtained with well-tuned RBF kernels are never worse and sometimes even statistically significantly better compared to results obtained with a linear kernel in terms of test set receiver operating characteristic and test set accuracy performances. (2) Even for classification with linear classifiers like LS-SVM with linear kernel, using regularization is very important. (3) When performing kernel principal component analysis (kernel PCA) before classification, using an RBF kernel for kernel PCA tends to result in overfitting, especially when using supervised feature selection. It has been observed that an optimal selection of a large number of features is often an indication for overfitting. Kernel PCA with linear kernel gives better results.

Algorithms↗
PubMed2003

INCLUSive: A web portal and service registry for microarray and regulatory sequence analysis.

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://www.esat.kuleuven.ac.be/inclusive.

Algorithms↗
PubMed2002

INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling.

INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes.

Algorithms↗
PubMed2002

Adaptive quality-based clustering of gene expression profiles.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

Algorithms↗