A5049306910- Biomedical Text Mining and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Bioinformatics
- vaccines and immunoinformatics approaches
- Genetics, Bioinformatics, and Biomedical Research
- Advanced Text Analysis Techniques
Dalian University of Technology
2021-2022
Abstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. related findings such as vaccine and drug development have reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation interpretation. For instance, LitCovid is literature database COVID-19-related PubMed, which accumulated more than 200 with millions accesses each month by users...
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research undertaken to understand the characteristics of virus and design vaccines drugs. related findings have reported in biomedical literature at a rate about 10,000 articles on per month. Such rapid growth significantly challenges manual curation interpretation. For instance, LitCovid is database COVID-19-related PubMed, which accumulated more than 200,000 with millions accesses each month by...
The ever-increasing volume of medical literature necessitates the classification literature. Medical relation extraction is a typical method classifying large With development arithmetic power, models have evolved from rule-based to neural network models. single model discards shallow syntactic information while discarding traditional rules. Therefore, we propose information-based that complements and equalizes enhance model.We aim complete for more efficient classification.We devised 2...
<sec> <title>BACKGROUND</title> The ever-increasing volume of medical literature necessitates the classification literature. Medical relation extraction is a typical method classifying large With development arithmetic power, models have evolved from rule-based to neural network models. single model discards shallow syntactic information while discarding traditional rules. Therefore, we propose information-based that complements and equalizes enhance model. </sec> <title>OBJECTIVE</title> We...