The relationship between corpus callosum and schizophrenia is elusive. Neuropsychiatric symptoms in Mild encephalitis with reversible splenial lesion (MERS) such as delirium, and negativism, suggest a link between corpus callosum and psychiatric disturbances.Here in, we report catatonia as an initial symptom of MERS in a schizophrenic patient.The aim of this study is to discuss the likely causal relationship between catatonic syndrome and MERS.To the best of our knowledge, the catatonia was not reported before as a prodromal symptom of MERS. We therefore report this case in order to enlarge the spectrum of MERS symptoms in psychiatric patients and discuss the relationship between catatonia and splenium lesions.

The behavior of many diseases is still not well understood by researchers. Genome-Wide Association (GWA) analyzes have recently become a popular approach to discovering the genetic causes of many complex diseases. These analyzes could lead to the discovery of genetic factors potentially involved in certain disease susceptibility. These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP). Indeed, many complex diseases have been revealed to be associated with combinations of SNP interactions. However, the identification of such interactions is considered difficult. Therefore, various unsupervised data mining methods are often developed in the literature to identify such variation involved in disease. In this work, a biclustering method is adopted to detect possible associations between SNP markers and disease susceptibility. It is an unsupervised classification technique, which plays an increasingly important role in the study of modern biology. We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association. An experimental study is achieved on synthetic data to evaluate the performance of the proposed algorithm. Promising results are obtained.

Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially on the assumption that the extraction of the co-expressed genes allows to have co-regulated genes. In addition, the integration of biological information in the search process may induce to the extraction of relevant and non-trivial biclusters. Therefore, this work proposes an evolutionary algorithm based on local search method that relies on biological knowledge. An experimental study is achieved on real microarray datasets to evaluate the performance of the proposed algorithm. The assessment and the comparison are based on statistical and biological criteria. A cross-validation experiment is also used to estimate its accuracy. Promising results are obtained. They demonstrate the importance of the integration of the biological knowledge in the biclustering process to foster the efficiency and to promote the discovery of non-trivial and biologically relevant biclusters.

The extraction of knowledge from large biological data is among the main challenges of bioinformatics. Several data mining techniques have been proposed to extract data; in this work, we focus on biclustering which has grown considerably in recent years. Biclustering aims to extract a set of genes with similar behavior under a condition set. In this paper, we propose an evolutionary biclustering algorithm and we analyze its performance by varying its genetic components. Hence, several versions of the evolutionary biclustering algorithm are introduced. Further, an experimental study is achieved on two real microarray datasets and the results are compared to other state-of-the-art biclustering algorithms. This thorough study allows to retain the best combination of operators among the various experienced choices.

Microarrays represent a new technology for measuring expression levels of several genes under various biological conditions generating multiple data. These data can be analyzed by using biclustering method which aims to extract a maximum number of genes and conditions presenting a similar behavior. This paper proposes a new evolutionary approach to obtain maximal high-quality biclusters of highly-correlated genes. The performance of the proposed algorithm is assessed on synthetic gene expression data. Experimental results show that our algorithm competes favorably with several state-of-the-art biclustering algorithms.