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    Machine learning to optimize CRISPR system

    A team of researchers from Microsoft and the Broad Institute of MIT and Harvard has developed a new system that allows researchers to more quickly and effectively use the powerful gene-editing tool CRISPR.
    The system, unveiled Monday and dubbed Azimuth, uses machine learning, in which a computer takes a limited set of training data and uses that to learn how to make predictions about data it hasn't yet seen. In this case, the machine learning system is being used to predict which part of a gene to target when a scientist wants to knockout – or shut off – a gene. Machine learning enables the model to make predictions for any gene of interest, including those not seen in the experimental training data.
    The research team published their findings this week in the journal Nature Biotechnology on Jan 18 2016. In addition to the computational modelling, the team also released screening libraries that will help scientists more easily identify which of the hundreds, if not thousands, of places within a gene they should target with CRISPR to get the result they want.
    The partnership allowed the two sets of researchers, who are working on the bleeding edge of machine learning and gene editing, respectively, to collaborate on ways to advance the revolutionary new CRISPR technology.