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Predicting the Risk of Schizophrenia Using Machine-Learning and a Blood Test


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Predicting the Risk of Schizophrenia Using Machine-Learning and a Blood Test

"As a scientist interested in applying machine learning to medicine, our findings are very exciting," said first author Dr. Chathura J. Gunasekara.

By applying the SPLS-DA machine-learning algorithm to analyze specific regions of the CoRSIVs human genome, a team led by researchers at Baylor College of Medicine has reported the possibility of early detection of schizophrenia. In DNA from blood samples, the team identified epigenetic markers, a profile of methyl chemical groups in the DNA, that differ between people diagnosed with schizophrenia and people without the disease and developed a model that would assess an individual's probability of having the condition.

Testing the model on an independent dataset revealed that it can identify schizophrenia patients with 80% accuracy. The study appears in the journal Translational Psychiatry.

From Baylor College of Medicine
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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