Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine

Author(s):  
Zuoxi Yang
2020 ◽  
Vol 21 (S19) ◽  
Author(s):  
Maciej Rybinski ◽  
Sarvnaz Karimi ◽  
Vincent Nguyen ◽  
Cecile Paris

Abstract Background Finding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles—or as we call them documents—by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine. Results To provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents. Conclusions We introduce the search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.


2018 ◽  
Vol 15 (6) ◽  
pp. 1797-1809 ◽  
Author(s):  
Bo Xu ◽  
Hongfei Lin ◽  
Yuan Lin ◽  
Yunlong Ma ◽  
Liang Yang ◽  
...  

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain


2016 ◽  
Vol 63 ◽  
pp. 379-389 ◽  
Author(s):  
Yanshan Wang ◽  
Stephen Wu ◽  
Dingcheng Li ◽  
Saeed Mehrabi ◽  
Hongfang Liu

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