Nor Danish Imran Bin Nor Hisham
Pronouns: He/Him
Research Mentor(s): Yongqun He
Research Mentor School/College/Department: / 0
Program:
Authors: Nor Danish Imran Bin Nor Hisham, Jie Zheng, Yongqun He
Session: Session 4: 1:40 pm – 2:30 pm
Poster: 81
Abstract
The increase in research papers annually across diverse fields, including vaccines, poses a challenge for researchers to stay updated of the latest developments (e.g., clinical trials, genetic makeup, and new components) without taking a significant amount of time. This research aims to solve the problem of having to go through dozens of academic papers to extract information. This research explores a solution to the problem by using a Natural Language Processing (NLP) model to analyze academia literature. The method used in this research is to use Vaccine Ontology (VO) and IDOBRU Ontology to extend the original database provided by the National Institute of Health (NIH) with domain-specific vaccine and brucellosis concepts. This extended database will be used with SemRep, a literature mining tool created by NIH, to extract any brucella- and vaccine-related information—such as vaccine-related immune responses, vaccine design, and brucellosis proteins—from a large subset of relevant published researches’ abstracts available on PubMed. Our study has expanded the current Metathesaurus with 10,000 new concepts with 8,000 new vaccine concepts and 2,000 brucella-related terms. This study also demonstrates its capability by using it to study Brucella vaccines on PubMed using SemRep. In the end, we’ve managed to learn about alternative vaccines that are in development and its efficiency in treating Brucellosis and also answering the question on what proteins interact with different kinds of vaccines in Brucellosis vaccine study. In conclusion, modification on the database used on SemRep allows for a more specific domain knowledge extraction from biomedical texts. For vaccinology researchers, efficient knowledge extraction could address vital questions, improving clinical trial success rates and identifying new gene or protein compositions for potential novel vaccine discovery. Additionally, this research demonstrates the customization potential of SemRep, allowing for a broader application and accelerating information gathering in various fields.
