↓ Skip to main content

Methodology for the inference of gene function from phenotype data

Overview of attention for article published in BMC Bioinformatics, December 2014
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

10 tweeters


3 Dimensions

Readers on

37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Methodology for the inference of gene function from phenotype data
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0405-z
Pubmed ID

Joao A Ascensao, Mary E Dolan, David P Hill, Judith A Blake


BackgroundBiomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.ResultsWe have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.ConclusionsWe show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 27%
Student > Ph. D. Student 6 16%
Student > Bachelor 4 11%
Student > Doctoral Student 3 8%
Student > Postgraduate 3 8%
Other 9 24%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 46%
Computer Science 5 14%
Biochemistry, Genetics and Molecular Biology 4 11%
Engineering 3 8%
Business, Management and Accounting 2 5%
Other 4 11%
Unknown 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 February 2015.
All research outputs
of 15,466,176 outputs
Outputs from BMC Bioinformatics
of 5,648 outputs
Outputs of similar age
of 304,824 outputs
Outputs of similar age from BMC Bioinformatics
of 326 outputs
Altmetric has tracked 15,466,176 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 5,648 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 304,824 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 326 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.