Biomedical subjects

Marcel Dettling

Publications and source records attributed to Marcel Dettling.

7 recordsLinked to original sources

PubMed2005

Searching for differentially expressed gene combinations.

We propose 'CorScor', a novel approach for identifying gene pairs with joint differential expression. This is defined as a situation with good phenotype discrimination in the bivariate, but not in the two marginal distributions. CorScor can be used to detect phenotype-related dependencies and interactions among genes. Our easily interpretable approach is scalable to current microarray dimensions and yields promising results on several cancer-gene-expression datasets.

Gene Expression Profiling↗

PubMed2004

BagBoosting for tumor classification with gene expression data.

MOTIVATION: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting. RESULTS: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data. AVAILABILITY: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.html.

Algorithms↗

PubMed2004

Bioconductor: open software development for computational biology and bioinformatics.

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

Computational Biology↗

PubMed2004

Gene expression signatures identify rhabdomyosarcoma subtypes and detect a novel t(2;2)(q35;p23) translocation fusing PAX3 to NCOA1.

Rhabdomyosarcoma is a pediatric tumor type, which is classified based on histological criteria into two major subgroups, namely embryonal rhabdomyosarcoma and alveolar rhabdomyosarcoma. The majority, but not all, alveolar rhabdomyosarcoma carry the specific PAX3(7)/FKHR-translocation, whereas there is no consistent genetic abnormality recognized in embryonal rhabdomyosarcoma. To gain additional insight into the genetic characteristics of these subtypes, we used oligonucleotide microarrays to measure the expression profiles of a group of 29 rhabdomyosarcoma biopsy samples (15 embryonal rhabdomyosarcoma, and 10 translocation-positive and 4 translocation-negative alveolar rhabdomyosarcoma). Hierarchical clustering revealed expression signatures clearly discriminating all three of the subgroups. Differentially expressed genes included several tyrosine kinases and G protein-coupled receptors, which might be amenable to pharmacological intervention. In addition, the alveolar rhabdomyosarcoma signature was used to classify an additional alveolar rhabdomyosarcoma case lacking any known PAX3 or PAX7 fusion as belonging to the translocation-positive group, leading to the identification of a novel translocation t(2;2)(q35;p23), which generates a fusion protein composed of PAX3 and the nuclear receptor coactivator NCOA1, having similar transactivation properties as PAX3/FKHR. These experiments demonstrate for the first time that gene expression profiling is capable of identifying novel chromosomal translocations.

Base Sequence↗

PubMed2004

Gene expression profiles and risk stratification in childhood acute lymphoblastic leukemia.

BACKGROUND AND OBJECTIVES: Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease. There are several distinct genetic subtypes, characterized by typical changes in gene expression pattern. In addition to cytogenetic markers, the in vivo response to treatment is an emerging prognostic marker for risk stratification. However, it has not yet been reported whether gene expression profiles can predict risk group stratification already at the time of diagnosis. DESIGN AND METHODS: We analyzed bone marrow samples of 31 ALL patients to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype. Gene expression profiles were established using oligonucleotide microarrays. RESULTS: Considering all low- and high-risk patients, no gene was capable of predicting the risk assignment already at time of diagnosis. However, screening for risk group associated genes using more homogeneous subsets of patients revealed 10(6) discriminatory probe sets. The prognostic significance of these probe sets was subsequently determined for the entire series of patients. Using the selected subgroups as the training set and the remaining samples as an independent test set, logistic regression using 3 predictor variables could accurately predict current risk assignment for 10 out of 12 patients. INTERPRETATION AND CONCLUSIONS: Gene expression profiles established from a cytogenetically heterogeneous study group are not, as yet, sufficiently accurate to be used prognostically in a clinical setting. Additional risk-associated gene expression analyses need to be performed in more homogeneous sets of patients.

Child↗

PubMed2003

Boosting for tumor classification with gene expression data.

MOTIVATION: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. RESULTS: We demonstrate that the generic boosting algorithm needs some modification to become an accurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets. AVAILABILITY: Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html.

Algorithms↗

PubMed2002

Supervised clustering of genes.

BACKGROUND: We focus on microarray data where experiments monitor gene expression in different tissues and where each experiment is equipped with an additional response variable such as a cancer type. Although the number of measured genes is in the thousands, it is assumed that only a few marker components of gene subsets determine the type of a tissue. Here we present a new method for finding such groups of genes by directly incorporating the response variables into the grouping process, yielding a supervised clustering algorithm for genes. RESULTS: An empirical study on eight publicly available microarray datasets shows that our algorithm identifies gene clusters with excellent predictive potential, often superior to classification with state-of-the-art methods based on single genes. Permutation tests and bootstrapping provide evidence that the output is reasonably stable and more than a noise artifact. CONCLUSIONS: In contrast to other methods such as hierarchical clustering, our algorithm identifies several gene clusters whose expression levels clearly distinguish the different tissue types. The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics.

Algorithms↗