Candidate gene prioritization by network analysis of differential expression using machine learning approaches
Background Discovering novel disease genes is still challenging for diseases for which no
prior knowledge-such as known disease genes or disease-related pathways-is available.
Performing genetic studies frequently results in large lists of candidate genes of which only
few can be followed up for further investigation. We have recently developed a
computational method for constitutional genetic disorders that identifies the most promising
candidate genes by replacing prior knowledge by experimental data of differential gene …
prior knowledge-such as known disease genes or disease-related pathways-is available.
Performing genetic studies frequently results in large lists of candidate genes of which only
few can be followed up for further investigation. We have recently developed a
computational method for constitutional genetic disorders that identifies the most promising
candidate genes by replacing prior knowledge by experimental data of differential gene …
[引用][C] Candidate gene prioritization by network analysis of differential expression using machine learning approaches
D Börnigen, J Goncalves, F Ojeda, B De Moor… - BMC …, 2010 - BioMed Central
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