Abstract
In modern biology, the use of high-throughput technologies allows researchers and practicians to quickly and efficiently screen the genome in order to identify the genetic factors of a given disorder.However these techniques are often generating large lists of candidate genes among which only one or a few are really associated to the biological process of interest. Since the individual validation of all these candidate genes is often too costly and time consuming, only the most promising genes are experimentally assayed.
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Yu, S., Tranchevent, LC., De Moor, B., Moreau, Y. (2011). Cross-Species Candidate Gene Prioritization with MerKator. In: Kernel-based Data Fusion for Machine Learning. Studies in Computational Intelligence, vol 345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19406-1_8
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DOI: https://doi.org/10.1007/978-3-642-19406-1_8
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