Curing genetic disease with Machine Learning
Many diseases afflicting humans are genetic in origin, meaning that they are the result of faulty activation or deactivation of parts of the genome. Many such conditions are difficult to treat effectively by conventional means, but the emerging field of gene therapy promises to provide a potential solution.
Developing gene therapies requires gaining a detailed understanding of the functioning of the human genome, specifically around developing a better understanding of which proteins in genes bind, and where they do this in the genome. By gathering and processing extensive experimental data, with the help of machine learning technology, such a picture is being developed.
Using machine learning to help process and the data allows it to be carried out at a much greater volume. Doing so reliably and accurately, however, relies on developing a rigorously trained machine learning model. In turn, obtaining a quality model depends on the preparation of a sufficient quantity of high-quality training data and a carefully guided training process.
For the Wetherall institute, Zegami serves as a high productivity tool for both preparing training data, and for providing feedback on results from models throughout the training process.
With its filtering and layout tools, Zegami’s interface offers an order-of-magnitude increase in the efficiency of these tasks compared to previous approaches. The gains are achieved by enabling humans to handle items in batches of similar specimens rather than one by one.
Zegami also serves as a platform on which to publish all results to ensure their findings are fully open and available for others to reproduce or conduct their own analyses.