Comparison of four similarity measures based on GO annotations for Gene Clustering

Al-Mubaid, Hisham; Nagar, Anurag

doi:10.1109/iscc.2008.4625763

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…In PPI prediction and validation, similarly, semantic similarity can be the sole approach ( 5 , 19 ) or be used to improve already existing techniques ( 20 ). Finally, the role of semantic similarity in the analysis of transcriptomics and proteomics data is mainly the improvement of clustering of co-expressed gene products ( 21–23 ).…”

Section: Biomedical Kgsmentioning

confidence: 99%

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

Cardoso

Sousa

Köhler³

et al. 2020

Database

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The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein–protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein–protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

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Section: Biomedical Kgsmentioning

confidence: 99%

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

Cardoso

Sousa

Köhler³

et al. 2020

Database

View full text Add to dashboard Cite

show abstract

“…The edge-based approach relies on the structure of the GO graph (Wu et al (2006), Chen et al (2007), Al-Mubaid & Nagar (2008)). Basically, the semantic similarity between two GO terms is defined as the distance between two connected terms, which is the length of a path connecting the two terms in the GO graph.…”

Section: 4mentioning

confidence: 99%

Metabolic Pathway Membership Inference using an Ontology-based Similarity Approach

Cartealy

Liao

2019

Proceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science

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Methods of Gene Ontology Term Similarity Analysis in Graph Database Environment

Stypka

Kozielski

2014

Communications in Computer and Information Science

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Comparison of four similarity measures based on GO annotations for Gene Clustering

Cited by 14 publications

References 13 publications

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

Metabolic Pathway Membership Inference using an Ontology-based Similarity Approach

Methods of Gene Ontology Term Similarity Analysis in Graph Database Environment

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