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A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

Priya S, Jiang G, Dasari S, Zimmermann MT, Wang C, Heflin J, Chute CG - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Manual curation for harvesting this evidence is intractable as it is error prone and time consuming.The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager.We evaluated the performance of the annotator in terms of precision and recall.

View Article: PubMed Central - PubMed

Affiliation: Mayo Clinic, Rochester, MN ; Lehigh University, Bethlehem, PA.

ABSTRACT
Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

No MeSH data available.


Related in: MedlinePlus

A SPARQL query template to retrieve information for leukemia trials that contain mutations of the type translocation. This query is executed over the datasets including our annotation ontology and LinkedCT.
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Related In: Results  -  Collection


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f2-2092277: A SPARQL query template to retrieve information for leukemia trials that contain mutations of the type translocation. This query is executed over the datasets including our annotation ontology and LinkedCT.

Mentions: We can store the annotation model in a triple store and query it using SPARQL14. We can use SPARQL to issue queries over distributed datasets, as shown in Figure 2.


A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

Priya S, Jiang G, Dasari S, Zimmermann MT, Wang C, Heflin J, Chute CG - AMIA Jt Summits Transl Sci Proc (2015)

A SPARQL query template to retrieve information for leukemia trials that contain mutations of the type translocation. This query is executed over the datasets including our annotation ontology and LinkedCT.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525254&req=5

f2-2092277: A SPARQL query template to retrieve information for leukemia trials that contain mutations of the type translocation. This query is executed over the datasets including our annotation ontology and LinkedCT.
Mentions: We can store the annotation model in a triple store and query it using SPARQL14. We can use SPARQL to issue queries over distributed datasets, as shown in Figure 2.

Bottom Line: Manual curation for harvesting this evidence is intractable as it is error prone and time consuming.The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager.We evaluated the performance of the annotator in terms of precision and recall.

View Article: PubMed Central - PubMed

Affiliation: Mayo Clinic, Rochester, MN ; Lehigh University, Bethlehem, PA.

ABSTRACT
Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

No MeSH data available.


Related in: MedlinePlus