Approximately 90 percent of genes undergo alternative splicing, and scientists estimate that variations in the splicing code make up anywhere between 10 and 50 percent of all disease-linked mutations. “When you have mutations in the regulatory code, things can go very wrong,” Frey said. Frey’s team used an approach called deep learning. Like any kind of machine-learning technique, the model tries to find a relationship between two sets of data. In this case, Frey’s team connected the human reference genome with rich data sets cataloging the amounts of different protein components in different tissues. (Just as two different cake recipes vary in their ratios of flour and sugar, brain cells and liver cells vary in how much of each protein they produce.) In essence, the algorithms trained a computational model to read instructions embedded in the DNA. wired/2014/12/machine-intelligence-cracks-genetic-controls/
Posted on: Tue, 30 Dec 2014 23:39:52 +0000
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