Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation

By Phillip W. Lord, Robert Stevens, Andy Brass, Carole A. Goble. 2003

947 citations recorded by the source corpus.

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In Genetics

In Molecular Biology

Citations in the corpus

Identification of functional modules using network topology and high-throughput data (2007, methodology): “The average expression homogeneity and the average functional homogeneity of the produced JACSs (computed as described in [1]) of the JACSs did not change by more than 6%.”
Semantic similarity methods in wordNet and their application to information retrieval on the web (2005, background): “their probability of occurrence in a corpus [8, 15, 6, 3].”
Concept annotation in the CRAFT corpus (2012, neutral): “…analyzing their use in annotations of genes/gene products in model-organism databases (and from this, the information content of these annotations) [62,63], the information content of biomedical concepts can be calculated by analyzing their use in annotations of textual mentions in biomedical…”
Metrics for GO based protein semantic similarity: a systematic evaluation (2008, methodology): “As for which semantic similarity measure is more suitable to GO, it raises another question: how do you evaluate the performance of semantic similarity measures? Authors have used correlations with sequence similarity [3,4], with Pfam similarity [10], with gene co-expression [9], and with protein in”
SVM-RFE With MRMR Filter for Gene Selection (2010, background): “between semantic similarity of gene pairs and their structuraland sequence-based similarity [30], [31].”
Coexpression analysis of human genes across many microarray data sets. (2004, methodology): “We also tested an alternative semantic similarity metric suggested by Lord et al. (2003).”
A High Quality Draft Consensus Sequence of the Genome of a Heterozygous Grapevine Variety (2007, methodology): “…the base for gene annotations GO terms were extracted from BLAST searches against KEGG databases, KOBAS of metabolic pathways and InterproScan [118] and clustered using their semantic similarity [119], accuracy weight and the path from the root node of the ontology to the most detailed annotation.”
Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions (2009, methodology): “In this paper we use a text-based similarity measure; alternative measures of relatedness, for example similarity in gene annotations or expression data, could be easily applied instead [25,26].”
Predicting protein function from sequence and structure (2007, methodology): “Similarities between the sets of annotation terms assigned to separate proteins can be automatically quantified using a 'semantic similarity measure' (Lord et al”
Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods (2009, background): “[42] and average all the possible similarity values between putative orthologs i and j, since in general a protein has all the attributed roles.”
Multiconstrained gene clustering based on generalized projections (2010, methodology): “Recently, the GO-based semantic similarity measures have been applied to searching semantically similar proteins [27], clustering gene expression data and assessing cluster validity [19,28,29], developing new human regulatory pathway modeling tools [30], validating protein interaction data [31], val”
The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families (2007, methodology): “These pairs were divided into into functionally related and unrelated clusters based on a measure of GO term similarity (p-value � 0.01) [ 146 ].”
Measures of semantic similarity and relatedness in the biomedical domain (2007, methodology): “[15] adapted WordNet-based measures of relatedness to the Gene Ontology [16], which is a highly specialized ontology of the molecular functions and biological processes of gene products.”