RT Journal Article
SR Electronic
T1 Synthesizer: Expediting synthesis studies from context-free data with natural language processing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 053629
DO 10.1101/053629
A1 Lisa Gandy
A1 Jordan Gumm
A1 Benjamin Fertig
A1 Michael J. Kennish
A1 Sameer Chavan
A1 Ann Thessen
A1 Luigi Marchionni
A1 Xiaoxan Xia
A1 Shambhavi Shankrit
A1 Elana J Fertig
YR 2016
UL http://biorxiv.org/content/early/2016/05/16/053629.abstract
AB Today’s low cost digital data provides unprecedented opportunities for scientific discovery from synthesis studies. For example, the medical field is revolutionizing patient care by creating personalized treatment plans based upon mining electronic medical records, imaging, and genomics data. Standardized annotations are essential to subsequent analyses for synthesis studies. However, accurately combining records from diverse studies requires tedious and error-prone human curation, posing a significant barrier to synthesis studies. We propose a novel natural language processing (NLP) algorithm, Synthesize, to merge data annotations automatically. Application to patient characteristics for diverse human cancers and ecological datasets demonstrates the accuracy of Synthesize in diverse scientific disciplines. This NLP approach is implemented in an open-source software package, Synthesizer. Synthesizer is a generalized, user-friendly system for error-free data merging.