Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations

2018 ◽  
Vol 15 (3) ◽  
pp. 905-912 ◽  
Author(s):  
Najmul Ikram ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Protein Sequence ◽  
Sequence Similarity ◽  
Protein Sequence Similarity

scholarly journals Using bioinformatic protein sequence similarity to investigate if SARS CoV-2 infection could cause an ocular autoimmune inflammatory reactions?

2021 ◽  
Vol 203 ◽  
pp. 108433
Author(s):  
Işıl Kutlutürk Karagöz ◽  
Marion R. Munk ◽  
Mücahit Kaya ◽  
René Rückert ◽  
Mustafa Yıldırım ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Sequence Similarity ◽  
Inflammatory Reactions ◽  
Protein Sequence Similarity

A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

2011 ◽  
Vol 09 (06) ◽  
pp. 681-695 ◽  
Author(s):  
MARCO A. ALVAREZ ◽  
CHANGHUI YAN
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Common Ancestor ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Similarity Score ◽  
Gene Products ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm ◽  
Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

scholarly journals Classification of Myoviridae bacteriophages using protein sequence similarity

2009 ◽  
Vol 9 (1) ◽  
pp. 224 ◽  
Author(s):  
Rob Lavigne ◽  
Paul Darius ◽  
Elizabeth J Summer ◽  
Donald Seto ◽  
Padmanabhan Mahadevan ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Sequence Similarity ◽  
Protein Sequence Similarity
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