A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study

Qin, Xuan; Yao, Xinzhi; Xia, Jingbo

doi:10.2196/28247

Cited by 3 publications

(1 citation statement)

References 25 publications

(37 reference statements)

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“…Text mining has been applied to many tasks in the biomedical domain, such as identifying genes and other named entities ( 4 ) in text and subsequently extracting associations between genes and other genes ( 5 ), pathways ( 6 ) and diseases ( 7 ). Many studies have focused on doing the latter based on biomedical abstracts only ( 8 ), whereas fewer have included full-text articles too ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

et al. 2022

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The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, Target Illumination by GWAS Analytics, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/Target Central Resource Database and the Cytoscape stringApp. All data in DISEASES are updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses. Database URL: https://diseases.jensenlab.org

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Section: Introductionmentioning

confidence: 99%

Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

et al. 2022

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DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

Grissa

Junge

Oprea

et al. 2021

Preprint

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The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

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Development and Study of a Knowledge Graph for Retrieving the Relationship Between BVDV and Related Genes

Bai

et al. 2023

CBIO

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Background: Bovine viral diarrhea virus（BVDV）can cause diarrhea, abortion, and immunosuppression in cattle, imposing huge economic losses for the global cattle industry. The pathogenic and immune mechanisms of BVDV remain elusive. The development of a BVDV-gene knowledge base can provide clues to reveal the interaction of BVDV with host cells. However, the traditional method of manually establishing a knowledge base is time-consuming and inefficient. The method of developing a knowledge base based on deep learning has noticeably attracted scholars' attention recently. background: Bovine viral diarrhea virus (BVDV) can cause damage to bovine intestinal wall, loss of villi, hindrance of growth and development, weight loss and other adverse effects. The construction of BVDV-gene knowledge base is of great significance for the prevention and treatment of bovine diarrhea diseases and the healthy development of dairy industry. Biomedical literature contains untapped biomedical knowledge, and the traditional method of manually constructing knowledge base is time-consuming and inefficient. Most of them use relational database to store data, which is not conducive to semantic information retrieval. Objective: The study aimed to explore the substitution of deep learning for manual mining of BVDV-related genes and to develop a knowledge graph of the relationship between BVDV and related genes. Methods: A deep learning-based biomedical knowledge graph development method was proposed, which used deep learning to mine biomedical knowledge, model BVDV and various gene concepts, and store data in a graphical database. First, the PubMed database was used as the data source and crawler technology to obtain abstract data on the relationship between BVDV and various host genes. Pre-trained BioBERT model was used for biomedical named entity recognition to obtain all types of gene entities, and the pre-trained BERT model was utilized for relationship extraction to achieve the relationship between BVDV and various gene entities. Then, it was combined with manual proofreading to obtain structured triple data with high accuracy. Finally, the Neo4j graph database was used to store data and to develop the knowledge graph of the relationship between BVDV and related genes. method: First of all, we use PubMed biomedical literature database as the data source and crawler technology to obtain literature abstract data of the relationship between BVDV and various genes. Pre-trained model BioBERT for biomedical named entity recognition to get all kinds of gene entities and pre-trained model BERT for relationship extraction to get the relationship between BVDV and various gene entities. And then combined with manual proofreading to obtain structured triple data with high accuracy. Finally, the Neo4j graph database is used to store the data, and the knowledge graph of the relationship between BVDV and all kinds of genes is constructed Results: The results showed the obtainment of 71 gene entity types, including PRL4, MMP-7, TGIF1, etc. 9 relation types of BVDV and gene entities were obtained, including "can downregulate expression of", "can upregulate expression of", "can suppress expression of", etc. The knowledge graph was developed using deep learning to mine biomedical knowledge combined with manual proofreading, which was faster and more efficient than the traditional method of establishing knowledge base manually, and the retrieval of semantic information by storing data in graph database was also more efficient. Conclusion: A BVDV-gene knowledge graph was preliminarily developed, which provided a basis for studying the interaction between BVDV and host cells. conclusion: The method of using the method of deep learning to mine biomedical knowledge combined with manual proofreading to construct knowledge graph is faster and more efficient than the traditional method of constructing knowledge base manually, and the retrieval of semantic information by storing data in graph database is also more efficient. other: The method of using the method of deep learning to mine biomedical knowledge combined with manual proofreading to construct knowledge graph is faster and more efficient than the traditional method of constructing knowledge base manually.

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A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study

Cited by 3 publications

References 25 publications

Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

Development and Study of a Knowledge Graph for Retrieving the Relationship Between BVDV and Related Genes

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