A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration.

ABSTRACT:

Background

In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: "molecular identification" (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, dir

SUBMITTER: Day RS 

PROVIDER: S-EPMC3734162 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature