Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

ABSTRACT: Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to

SUBMITTER: Breckels LM 

PROVIDER: S-EPMC4866734 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature