Integration of probabilistic functional networks without an external Gold Standard

James, Katherine, Alsobhe, Aoesha, Cockell, Simon J., Wipat, Anil and Pocock, Matthew (2022) Integration of probabilistic functional networks without an external Gold Standard. BMC Bioinformatics, 23 (1). p. 302. ISSN 1471-2105

s12859-022-04834-4.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview
Official URL:


Background: Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. Results: We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. Conclusions: The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.

Item Type: Article
Additional Information: Funding information: AA is sponsored by Saudi Electronic University.
Uncontrolled Keywords: Bioinformatics, Gold Standards, Interactome, Network integration, Probabilistic functional integrated networks, Protein function prediction
Subjects: C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Rachel Branson
Date Deposited: 02 Aug 2022 14:57
Last Modified: 02 Aug 2022 15:00

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics