Gene Functional Similarity Analysis by Definition-based Semantic Similarity Measurement of GO Terms

Pesaranghader, Ahmad; Pesaranghader, Ali; Rezaei, Azadeh; Davoodi, Danoosh

doi:10.1007/978-3-319-06483-3_18

“…Gene expression data is one of such measures. Also, it is known that the genes involved in the same biological category, show similar expression pattern [7,[24][25][26].…”

Section: Discussionmentioning

confidence: 99%

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Lakizadeh

¹

,

Jalili

²

2017

Zahedan J Res Med Sci

0

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Background: Gene ontology (GO) is a well-structured knowledge of biological terms that describes roles of genes and their products in a standardized and organized controlled vocabulary format. Over the last decade, many measures are developed to exploit GO advantages to determine semantic similarities between biological entities. Using GO ontologies, there are some constraints that existing GO-based semantic similarity measures try to address them. For instance, (1) edges in a GO graph, do not indicate uniform distances and also have different densities, and (2) ignoring term levels in an ontology makes "shallow annotation" drawback, i.e., two terms with a certain distance near the root of GO graph have equal semantic similarity with two terms with the same distance but far from the root.

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“…Functional-based methods of semantic similarity generally associate a set of biomolecules (e.g. genes) to each disease and compare the sets for quantifying the similarity [14]. Wang's method determines the semantic similarity of two terms (genes, Gene Ontology or Disease Ontology) based on both the locations of these terms in a hierarchical graph and their relations with their ancestor terms [15].…”

Section: Gene Set Enrichment Analysis and Semantic Similaritymentioning

confidence: 99%

Reusing microarray clinical data from a complex disease with bioinformatics tool

Prete

¹

,

Facchiano

²

,

Lió

³

2018

Preprint

0

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Clinical bioinformatics, translational bioinformatics and personalised medicine are connected by the common task of analysing and integrating clinical data and results, in order to find important biomarkers related to pathologies and facilitate their prediction, diagnosis and treatment. New technologies provides the possibility to have more and more clinical data available in online databases. This data can be reused for studying complex disease from novel point of views. This work show how it is possible considering online microarray data from coeliac disease and some of its comorbidities, combining both the data and the results. The main goal is the extraction of common evidences among the selected pathologies, from genes to different kinds of functional annotation, showing which biological processes are more involved in these autoimmune disorders and quantifying the similarity between coeliac disease and its comorbidities. The pipeline of the work is developed in R language, and it is semi-automated. Methodologically, the advantage of this work is the possibility of performing the entire analysis starting from a different pathology; clinically, scientists can have the possibility of using data already published to highlight old and new evidences, with the possibility of improve the knowledge on a complex disease according to the availability of new microarray data.

show abstract

“…Functional-based methods of semantic similarity generally associate a set of biomolecules (e.g. genes) to each disease and compare the sets for quantifying the similarity [14]. Wang's method determines the semantic similarity of two terms (genes, Gene Ontology or Disease Ontology) based on both the locations of these terms in a hierarchical graph and their relations with their ancestor terms [15].…”

Section: Gene Set Enrichment Analysis and Semantic Similaritymentioning

confidence: 99%

Reusing microarray clinical data from a complex disease with bioinformatics tool

Prete

¹

,

Facchiano

²

,

Lió

³

2018

Preprint

0

View full text Add to dashboard Cite

Clinical bioinformatics, translational bioinformatics and personalised medicine are connected by the common task of analysing and integrating clinical data and results, in order to find important biomarkers related to pathologies and facilitate their prediction, diagnosis and treatment. New technologies provides the possibility to have more and more clinical data available in online databases. This data can be reused for studying complex disease from novel point of views. This work show how it is possible considering online microarray data from coeliac disease and some of its comorbidities, combining both the data and the results. The main goal is the extraction of common evidences among the selected pathologies, from genes to different kinds of functional annotation, showing which biological processes are more involved in these autoimmune disorders and quantifying the similarity between coeliac disease and its comorbidities. The pipeline of the work is developed in R language, and it is semi-automated. Methodologically, the advantage of this work is the possibility of performing the entire analysis starting from a different pathology; clinically, scientists can have the possibility of using data already published to highlight old and new evidences, with the possibility of improve the knowledge on a complex disease according to the availability of new microarray data.

show abstract

Gene Functional Similarity Analysis by Definition-based Semantic Similarity Measurement of GO Terms

Cited by 8 publications

References 9 publications

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Reusing microarray clinical data from a complex disease with bioinformatics tool

Reusing microarray clinical data from a complex disease with bioinformatics tool

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