Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques.

ABSTRACT: Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100

SUBMITTER: Gandy LM 

PROVIDER: S-EPMC5402950 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature