Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lor parts of GO trees are full of terms with more specific semantics. Methods This study proposed a method of measuring semantic similarities beten GO terms using the entire GO tree structure, including both the upper (ancestral) and the lor (descendant) parts. Comprehensive comparison studies re performed with ll-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, proteineprotein interactions, and biological pathway analyses. Conclusion The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.
['Sang Jay Bien', 'Chan Hee Park', 'Hae Jin Shim', 'Woongcheol Yang', 'Jihun Kim', 'Ju Han Kim']
I am writing a research paper on a idea Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses
[{"from": "human", "value": "I am writing a research paper on a idea Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lor parts of GO trees are full of terms with more specific semantics. Methods This study proposed a method of measuring semantic similarities beten GO terms using the entire GO tree structure, including both the upper (ancestral) and the lor (descendant) parts. Comprehensive comparison studies re performed with ll-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, proteineprotein interactions, and biological pathway analyses. Conclusion The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods."}]