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Computational tools for prioritizing candidate genes : boosting disease gene discovery
Tijdschriftbijdrage - Tijdschriftartikel
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.
Tijdschrift: Nature Reviews. Genetics
Pagina's: 523 - 536
Jaar van publicatie:2012