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245	1	0	_aComputational tools for prioritizing candidate genes: boosting disease gene discovery
490	0		_vNature Reviews Genetics, 13, p.523-536, 2012
520	3		_aAt 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.
700	1	2	_aYves, Moreau
700	1	2	_aTranchevent, L-C.
856	4	0	_uhttps://drive.google.com/file/d/1M-uigw8lwJ7TxBu1esxuFE4A5NSVZ1Cs/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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