HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning
Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.799349/full |
| _version_ | 1828848125107765248 |
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| author | Xian Zhu Xian Zhu Yueming Gu Zhifeng Xiao |
| author_facet | Xian Zhu Xian Zhu Yueming Gu Zhifeng Xiao |
| author_sort | Xian Zhu |
| collection | DOAJ |
| description | Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG. |
| first_indexed | 2024-12-12T22:23:55Z |
| format | Article |
| id | doaj.art-2b3e79373e5745699475aeabf849bbbb |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-12T22:23:55Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-2b3e79373e5745699475aeabf849bbbb2022-12-22T00:09:49ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-04-011310.3389/fgene.2022.799349799349HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer LearningXian Zhu0Xian Zhu1Yueming Gu2Zhifeng Xiao3School of Information Management, Nanjing University, Nanjing, ChinaSchool of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, ChinaSchool of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, AustraliaSchool of Engineering, Penn State Erie, The Behrend College, Erie, PA, United StatesRecent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.https://www.frontiersin.org/articles/10.3389/fgene.2022.799349/fullbiobertknowledge graphherbchemicaldiseasegene |
| spellingShingle | Xian Zhu Xian Zhu Yueming Gu Zhifeng Xiao HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning Frontiers in Genetics biobert knowledge graph herb chemical disease gene |
| title | HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning |
| title_full | HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning |
| title_fullStr | HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning |
| title_full_unstemmed | HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning |
| title_short | HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning |
| title_sort | herbkg constructing a herbal molecular medicine knowledge graph using a two stage framework based on deep transfer learning |
| topic | biobert knowledge graph herb chemical disease gene |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.799349/full |
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