Data mining detects signs of Lou Gehrig's disease in gene carriers long before symptoms appear
A report in the August issue of Behavioral Neuroscience, published by the American Psychological Association, describes how this novel use of data mining may enable investigators to test therapies to delay or even prevent disease, starting with hereditary forms.
Data mining to detect the subtle behavioral expression of mutations could conceivably allow investigators to test therapies aimed at preventing, slowing or stopping disease. By being able to predict more accurately which carriers may express the disease before they experience symptoms (the "premorbid" state), researchers could test medicines that might prevent symptoms from emerging.
Methods such as data mining can be therapeutically useful even before science understands how disease begins. The authors wrote, "The discovery of reliable behavioral endpoints with predictive validity, even before a good understanding of their etiology is achieved, can significantly improve intervention research."
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Data mining to detect the subtle behavioral expression of mutations could conceivably allow investigators to test therapies aimed at preventing, slowing or stopping disease. By being able to predict more accurately which carriers may express the disease before they experience symptoms (the "premorbid" state), researchers could test medicines that might prevent symptoms from emerging.
Methods such as data mining can be therapeutically useful even before science understands how disease begins. The authors wrote, "The discovery of reliable behavioral endpoints with predictive validity, even before a good understanding of their etiology is achieved, can significantly improve intervention research."
Do you want to know more?
Be a part of the XTractor community. XTractor is the first of its kind - Literature alert service, that provides manually curated and annotated sentences for the Keywords of user preference. XTractor maps the extracted entities (genes, processes, drugs, diseases etc) to multiple ontologies and enables customized report generation. With XTractor the sentences are categorized into biological significant relationships and it also provides the user with the ability to create his own database for a set of Key terms. Also the user could change the Keywords of preference from time to time, with changing research needs. The categorized sentences could then be tagged and shared across multiple users. Thus XTractor proves to be a platform for getting real-time highly accurate data along with the ability to Share and collaborate.
Sign up it's free, and takes less than a minute. Just click here:www.xtractor.in.
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