mProphet: automated data processing and statistical validation for large-scale SRMSRMSRM experiments

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

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Natural Language Processing to Play Major Role in Bringing Watson into Clinics

Under the terms of a recently inked agreement between IBM And Nuance, Watson's deep question answering, natural language processing, and machine learning capabilities will be linked with Nuance's speech recognition and Clinical Language Understanding, CLU, solutions to help physicians more accurately diagnose and treat their patients (BI02/11/2011).

In the months leading up to the first offerings from the collaboration, researchers at IBM and Nuance will work with collaborators at Columbia University and the University of Maryland, to figure out how Watson can best help in the clinical setting as well as to incorporate some healthcare-specific adaptations to the system, Jennifer Chu-Carroll, a member of the Watson Research Team, told BioInform.

"For the most part, the natural language analytics, the machine learning and the whole architecture are domain independent so we expect to be able plug these into the medical domain," she said. However, "there [will] be some ... research and development that is specific to the medical domain that we are going to have to bring in."

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