Abstract
Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of “big data” provides unprecedented opportunities for data-driven research and “big picture” models. However, in-depth analyses—making use of various data types and data sources and extracting knowledge—have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.
| Original language | English (US) |
|---|---|
| Title of host publication | Methods in Molecular Biology |
| Publisher | Humana Press Inc. |
| Pages | 49-69 |
| Number of pages | 21 |
| DOIs | |
| State | Published - Jan 1 2019 |
Publication series
| Name | Methods in Molecular Biology |
|---|---|
| Volume | 1939 |
| ISSN (Print) | 1064-3745 |
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Keywords
- Big data
- Drug target ontology
- KNARM
- Knowledge acquisition
- Ontology
- Semantic model
- Semantic web
ASJC Scopus subject areas
- Molecular Biology
- Genetics
Cite this
How to develop a drug target ontology : KNowledge acquisition and representation methodology (KNARM). / Küçük McGinty, Hande; Visser, Ubbo E; Schuerer, Stephan C.
Methods in Molecular Biology. Humana Press Inc., 2019. p. 49-69 (Methods in Molecular Biology; Vol. 1939).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - How to develop a drug target ontology
T2 - KNowledge acquisition and representation methodology (KNARM)
AU - Küçük McGinty, Hande
AU - Visser, Ubbo E
AU - Schuerer, Stephan C
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of “big data” provides unprecedented opportunities for data-driven research and “big picture” models. However, in-depth analyses—making use of various data types and data sources and extracting knowledge—have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.
AB - Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of “big data” provides unprecedented opportunities for data-driven research and “big picture” models. However, in-depth analyses—making use of various data types and data sources and extracting knowledge—have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.
KW - Big data
KW - Drug target ontology
KW - KNARM
KW - Knowledge acquisition
KW - Ontology
KW - Semantic model
KW - Semantic web
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UR - http://www.scopus.com/inward/citedby.url?scp=85062599323&partnerID=8YFLogxK
U2 - 10.1007/978-1-4939-9089-4_4
DO - 10.1007/978-1-4939-9089-4_4
M3 - Chapter
T3 - Methods in Molecular Biology
SP - 49
EP - 69
BT - Methods in Molecular Biology
PB - Humana Press Inc.
ER -