Computational tools for prioritizing candidate genes : boosting disease gene discovery

At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.

Jaar van publicatie:2012

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:10

CSS-citation score:4

Authors from:Higher Education