English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Characterizing genes with semantic information is an important processregarding the description of gene products. In spite that complete genomes ofmany organisms have been already sequenced, the biological functions of all oftheir genes are still unknown. Since experimentally studying the functions ofthose genes, one by one, would be unfeasible, new computational methods forgene functions inference are needed. We present here a novel computationalapproach for inferring biological function for a set of genes with previouslyunknown function, given a set of genes with well-known information. Thisapproach is based on the premise that genes with similar behaviour should begrouped together. This is known as the guilt-by-association principle. Thus, itis possible to take advantage of clustering techniques to obtain groups ofunknown genes that are co-clustered with genes that have well-known semanticinformation (GO annotations). Meaningful knowledge to infer unknown semanticinformation can therefore be provided by these well-known genes. We provide amethod to explore the potential function of new genes according to thosecurrently annotated. The results obtained indicate that the proposed approachcould be a useful and effective tool when used by biologists to guide theinference of biological functions for recently discovered genes. Our work setsan important landmark in the field of identifying unknown gene functionsthrough clustering, using an external source of biological input. A simple webinterface to this proposal can be found athttp://fich.unl.edu.ar/sinc/webdemo/gamma-am/.

Inferring unknown biological function by integration of GO annotations and gene expression data