Evidence map›Paper›PMID 28155711›Full record

ArticleBMC systems biology2016

Interspecies gene function prediction using semantic similarity.

Guoxian Yu, Wei Luo, Guangyuan Fu, Jun Wang

Abstract read
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Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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8citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.Database : the journal of biological databases and curation · 2020
    Article
  5. Article
  6. Article
  7. Article
  8. Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Guoxian YuCollege of Computer and Information Sciences, Southwest University, Chongqing, China. gxyu@swu.edu.cn.
Wei LuoCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
Guangyuan FuCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
Jun WangCollege of Computer and Information Sciences, Southwest University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGene Ontology (GO) is a collaborative project that maintains and develops controlled vocabulary (or terms) to describe the molecular function, biological roles and cellular location of gene products in a hierarchical ontology. GO also provides GO annotations that associate genes with GO terms. GO consortium independently and collaboratively annotate terms to gene products, mainly from model organisms (or species) they are interested in. Due to experiment ethics, research interests of biologists and resources limitations, homologous genes from different species currently are annotated with different terms. These differences can be more attributed to incomplete annotations of genes than to functional difference between them.

resultsSemantic similarity between genes is derived from GO hierarchy and annotations of genes. It is positively correlated with the similarity derived from various types of biological data and has been applied to predict gene function. In this paper, we investigate whether it is possible to replenish annotations of incompletely annotated genes by using semantic similarity between genes from two species with homology. For this investigation, we utilize three representative semantic similarity metrics to compute similarity between genes from two species. Next, we determine the k nearest neighborhood genes from the two species based on the chosen metric and then use terms annotated to k neighbors of a gene to replenish annotations of that gene. We perform experiments on archived (from Jan-2014 to Jan-2016) GO annotations of four species (Human, Mouse, Danio rerio and Arabidopsis thaliana) to assess the contribution of semantic similarity between genes from different species. The experimental results demonstrate that: (1) semantic similarity between genes from homologous species contributes much more on the improved accuracy (by 53.22%) than genes from single species alone, and genes from two species with low homology; (2) GO annotations of genes from homologous species are complementary to each other.

conclusionsOur study shows that semantic similarity based interspecies gene function annotation from homologous species is more prominent than traditional intraspecies approaches. This work can promote more research on semantic similarity based function prediction across species.

Indexed as

Gene OntologySemanticsAnimalsComputational BiologyDatabases, GeneticHumansMiceMolecular Sequence AnnotationNatural Language ProcessingSaccharomyces cerevisiaeSpecies SpecificityZebrafishGene function predictionGO annotationsInterspeciesSemantic similarity

Identifiers

PMID28155711
PMCPMC5260010

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.