Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | K-CAP 2021 |
| Untertitel | Proceedings of the 11th Knowledge Capture Conference |
| Seiten | 33-40 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781450384575 |
| Publikationsstatus | Veröffentlicht - 2 Dez. 2021 |
| Veranstaltung | 11th ACM International Conference on Knowledge Capture, K-CAP 2021 - Virtual, Online, USA / Vereinigte Staaten Dauer: 2 Dez. 2021 → 3 Dez. 2021 |
Abstract
Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide a wealth of information on drugs that can be exploited to enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions in the public database are focused on pairs of drugs. Since current treatments are composed of multi-drugs, it is extremely challenging to know which potential drugs affect the effectiveness of the treatment. In this work, we tackle the problem of discovering DDIs and reduce this problem to link prediction over a property graph represented in RDF-star. A deductive system captures knowledge about the conditions that define when a group of drugs interacts as Datalog rules. Extensional statements represent the property graph. Lastly, the intensional rules guide the deduction process to discover relationships in the graph and their properties. As a proof concept, we have implemented a graph traversal method on top of the property graph and the deduced edges. The technique aims to identify the combination of drugs whose interactions may reduce the effectiveness of a treatment or increase the number of toxicities. This traversal method relies on the computation of wedges in the property graph. Albeit illustrated in the context of DDI, this method could be generalized to other link traversal tasks. We conduct an experimental study on a DDIs property graph for different treatments. The results suggest that by capturing knowledge about DDIs, our approach can discover the drugs that decrease the effectiveness of the treatment. Our results are promising and suggest that clinicians can better understand the DDIs in treatment and prescribe improved treatments through the knowledge captured by our approach.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Software
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K-CAP 2021 : Proceedings of the 11th Knowledge Capture Conference. 2021. S. 33-40.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Capturing Knowledge about Drug-Drug Interactions to Enhance Treatment Effectiveness
AU - Rivas, Ariam
AU - Vidal, Maria Esther
N1 - Funding Information: Ariam Rivas is supported by the German Academic Exchange Service (DAAD). The authors thank the BIOMEDAS program for training. This work has been partially supported by the EU H2020 RIA funded projects CLARIFY with grant agreement No 875160 and EraMed P4-LUCAT No 53000015.
PY - 2021/12/2
Y1 - 2021/12/2
N2 - Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide a wealth of information on drugs that can be exploited to enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions in the public database are focused on pairs of drugs. Since current treatments are composed of multi-drugs, it is extremely challenging to know which potential drugs affect the effectiveness of the treatment. In this work, we tackle the problem of discovering DDIs and reduce this problem to link prediction over a property graph represented in RDF-star. A deductive system captures knowledge about the conditions that define when a group of drugs interacts as Datalog rules. Extensional statements represent the property graph. Lastly, the intensional rules guide the deduction process to discover relationships in the graph and their properties. As a proof concept, we have implemented a graph traversal method on top of the property graph and the deduced edges. The technique aims to identify the combination of drugs whose interactions may reduce the effectiveness of a treatment or increase the number of toxicities. This traversal method relies on the computation of wedges in the property graph. Albeit illustrated in the context of DDI, this method could be generalized to other link traversal tasks. We conduct an experimental study on a DDIs property graph for different treatments. The results suggest that by capturing knowledge about DDIs, our approach can discover the drugs that decrease the effectiveness of the treatment. Our results are promising and suggest that clinicians can better understand the DDIs in treatment and prescribe improved treatments through the knowledge captured by our approach.
AB - Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide a wealth of information on drugs that can be exploited to enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions in the public database are focused on pairs of drugs. Since current treatments are composed of multi-drugs, it is extremely challenging to know which potential drugs affect the effectiveness of the treatment. In this work, we tackle the problem of discovering DDIs and reduce this problem to link prediction over a property graph represented in RDF-star. A deductive system captures knowledge about the conditions that define when a group of drugs interacts as Datalog rules. Extensional statements represent the property graph. Lastly, the intensional rules guide the deduction process to discover relationships in the graph and their properties. As a proof concept, we have implemented a graph traversal method on top of the property graph and the deduced edges. The technique aims to identify the combination of drugs whose interactions may reduce the effectiveness of a treatment or increase the number of toxicities. This traversal method relies on the computation of wedges in the property graph. Albeit illustrated in the context of DDI, this method could be generalized to other link traversal tasks. We conduct an experimental study on a DDIs property graph for different treatments. The results suggest that by capturing knowledge about DDIs, our approach can discover the drugs that decrease the effectiveness of the treatment. Our results are promising and suggest that clinicians can better understand the DDIs in treatment and prescribe improved treatments through the knowledge captured by our approach.
KW - deductive system
KW - drug-drug interaction
KW - property graph
KW - wedge
UR - http://www.scopus.com/inward/record.url?scp=85120897210&partnerID=8YFLogxK
U2 - 10.1145/3460210.3493560
DO - 10.1145/3460210.3493560
M3 - Conference contribution
AN - SCOPUS:85120897210
SP - 33
EP - 40
BT - K-CAP 2021
T2 - 11th ACM International Conference on Knowledge Capture, K-CAP 2021
Y2 - 2 December 2021 through 3 December 2021
ER -