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MBORS: Mosquito vector Biocontrol Ontology and Recommendation System
G Jeyakodi1, P Shanthi Bala, O T Sruthi
1Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India.
Journal of Vector Borne Diseases
|April 22, 2024
Summary
This study introduces a Mosquito vector-based Biocontrol Ontology Recommendation System (MBORS) to combat vector-borne diseases using harmless biocontrol agents. The system provides knowledge on mosquito biocontrol, aiding early-stage vector management and disease prevention.
Area of Science:
- Biotechnology
- Entomology
- Public Health
Background:
- Mosquitoes transmit severe diseases like dengue, Zika, and malaria.
- Synthetic insecticides pose health risks, necessitating safer control methods.
- Biocontrol agents offer a non-toxic alternative for mosquito population management.
Purpose of the Study:
- To develop a Mosquito vector-based Biocontrol Ontology Recommendation System (MBORS).
- To facilitate mosquito biocontrol through an ontology linking species, diseases, and agents.
- To support early-stage vector control and raise community awareness.
Main Methods:
- Utilized Natural Language Processing and data mining techniques.
- Applied Tokenization, Part-of-speech Tagging, Named Entity Recognition, and rule-based text mining.
- Employed the Apriori algorithm for association rule mining.
Main Results:
- Developed the Mosquito Vector Association ontology (MVAont) in OWL format.
- Created the Mosquito vector biocontrol agents (MosqVecRS) recommendation system as an Android application.
- Made the ontology and recommendation system publicly accessible online.
Conclusions:
- MVAont predicts safe biocontrol agents to reduce vector-borne disease incidence.
- MosqVecRS enhances awareness and recommends effective biocontrol strategies for researchers and control communities.
