Text mining helps researchers ID Alzheimer's biomarkers

Researchers in the U.K. have found searching for Alzheimer's disease biomarkers can be improved through the use of text mining. In a study published in the Journal of Translational Medicine, the authors were able to identify 25 biomarker candidates via the data mining of publicly available databases, and say the practice could be applied to other disorders.

"We prove that text mining works, and we will take this forward in our hunt for Alzheimer's biomarkers," lead author Simon Lovestone, a professor of old age psychiatry at the King's College London's Institute of Psychiatry, said in a statement. "Our results also demonstrate the value of large data in biomedical science; you could go beyond Alzheimer's disease, and use the same approach for other conditions where biomarkers are needed, from cancer to diabetes."

After developing statements about what a blood biomarker looks like, the researchers used text and linguistic analysis to develop the computer code that was applied to the databases.

Just this week, four Johns Hopkins professors wrote an article on the increasing use computational models in healthcare. Their article, published in the journal Science Translational Medicine, concluded that such models are helping researchers to both identify and treat complex diseases.

An algorithm developed by scientists at New York-based Mount Sinai School of Medicine, meanwhile, is assisting in the building of networks from data found in medical records by helping researchers better understand interactions like gene-gene, protein-protein and drug side effects.

To learn more:
- here's the study
- read the King's College announcement


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