High-precision high-coverage functional inference from integrated data sources

Background: Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation. Results: We first apply this framework to Saccharomyces cerevisiae. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Na

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Bolan Linghu, Evan S. Snitkin, Dustin T. Holloway, Adam M. Gustafson, Yu Xia, Charles DeLisi
• Keywords: BMCBI 2008, Data Sources, Functional Annotation, Precise Functional Annotation