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Using an Algorithm to Solve Disease Mysteries


Dartmouth researchers have developed an algorithm that may someday be used to analyze blood for diagnostic purposes. Using data from a mass spectrometer, a device that generates a molecular fingerprint of biological samples, the Dartmouth team’s calculations can distinguish healthy blood from diseased blood.

This study by Ryan Lilien, a Dartmouth M.D./Ph.D. student; Hany Farid, Assistant Professor of Computer Science; and Bruce Donald, the Foley Professor of Computer Science, appeared in the Journal of Computational Biology.

Mathematical computations are routinely developed, varied and refined to analyze mass spectrometry data. The algorithm Q5 uses mathematical techniques called Principal Component Analysis and Linear Discriminant Analysis to differentiate between the mass spectra of healthy and diseased blood samples, and Q5 learns with each sample it tests, resulting in better accuracy. The algorithm compares the molecular fingerprint of each sample to identify features that differ between the healthy and disease states.

The researchers explain that there is much still to be learned from the different types of information within a sample of blood, and Q5 is one means of extracting new and important data.

Using data from a mass spectrometer, a device that generates a molecular fingerprint of biological samples, the Dartmouth team’s calculations can distinguish healthy blood from diseased blood.

 
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