Segregation: theoretical approach. an empirical analysis of gender segregation in undergraduate studies at the University of the Basque Country.

The aim of this study is to analyze the gender segregation in undergraduate studies in the University from Basque Country (UPV/EHU). We use data from UPV/EHU for the period 2003-2013. We focus on the period from 2003 to 2013 to analyze the changes in the segregation over ten years. We analyze the tendencies of the men and the women inside undergraduate studies. Undergraduate studies are decomposed into five fields: Legal and social sciences, experimental sciences, engineering, arts and humanities, and health sciences. We draw segregation curves and compute the Gini segregation index within the Lorenz approach. Our results show that the gender segregation in undergraduate studies in the UPV/EHU has decreased from 2003 to 2013.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.