Fabrice Leroy | PubMed Health

PubMed2005

Dynamical quantification of schizophrenic speech.

Schizophrenic speech has been studied both at the clinical and linguistic level. Nevertheless, the statistical methods used in these studies do not specifically take into account the dynamical aspects of language. In the present study, we quantify the dynamical properties of linguistic production in schizophrenic and control subjects. Subjects' recall of a short story was encoded according to the succession of macro- and micro-propositions, and symbolic dynamical methods were used to analyze these data. Our results show the presence of a significant temporal organization in subjects' speech. Taking this structure into account, we show that schizophrenics connect micro-propositions significantly more often than controls. This impairment in accessing language at the highest level supports the hypothesis of a deficit in maintaining a discourse plan in schizophrenia.

Adult↗

PubMed2005

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.

Algorithms↗