Automated Construction of a Knowledge Graph for Germline Gene-Disease Associations from Biomedical Literature

핵심 개념

Automated construction of a knowledge graph, SimpleGermKG, that connects germline genes and diseases extracted from biomedical literature using BioBERT and a hybrid ontology-rule-based approach.

초록

The paper presents an automated approach, SimpleGermKG, for constructing a knowledge graph that connects germline genes and diseases extracted from biomedical literature. The workflow consists of the following steps:

1. Data Pre-processing: The abstracts are tokenized and prepared for machine learning using the PunktSentenceTokenizer from NLTK.

2. Named Entity Recognition: BioBERT, a pre-trained BERT model on biomedical corpora, is used to extract genes and diseases from the germline abstracts.

3. Named Entity Normalization: An ontology-based and rule-based algorithm is developed to standardize and disambiguate the extracted gene and disease entities by mapping them to master terms from curated dictionaries.

4. Semantic Relation: A part-whole relation approach is implemented to connect the normalized gene and disease entities with their corresponding PubMed IDs, forming a knowledge graph.

The resulting SimpleGermKG contains 297 genes, 130 diseases, and 46,747 triples, which can be visualized and queried using the Neo4j graph database. The authors discuss the potential applications, limitations, and future research directions for the germline corpora knowledge graph.

통계

The germline corpora contain 11,261 abstracts from PubMed, with 114,311 sentences after tokenization.
The BioBERT-NER approach identified 19,751 gene entities and 19,135 disease entities.
The final SimpleGermKG contains 297 genes, 130 diseases, and 46,747 triples.

인용구

None

핵심 통찰 요약

Applying BioBERT to Extract Germline Gene-Disease Associations for Building a Knowledge Graph from the Biomedical Literature

by Armando D. D... 게시일 arxiv.org 04-24-2024

https://arxiv.org/pdf/2309.13061.pdf

더 깊은 질문

How can the SimpleGermKG be integrated with electronic health records, genomic data, and other existing biomedical ontologies to further enhance its capabilities and applications

To enhance the capabilities and applications of SimpleGermKG, integration with electronic health records (EHRs), genomic data, and existing biomedical ontologies is crucial. By incorporating data from EHRs, SimpleGermKG can provide a comprehensive view of a patient's medical history, treatment outcomes, and genetic predispositions. This integration allows for a more personalized approach to healthcare by linking germline gene-disease associations with individual patient data. Genomic data integration enables the correlation of genetic variations with disease susceptibility, facilitating the identification of potential risk factors and personalized treatment strategies. Additionally, leveraging existing biomedical ontologies ensures consistency and interoperability with established domain knowledge, enabling researchers and clinicians to access a wide range of structured information for analysis and decision-making. By integrating these diverse sources of data, SimpleGermKG can offer a holistic view of germline gene-disease associations and their implications for personalized medicine.

What are the potential challenges and limitations in applying conventional relation extraction techniques to identify the complex relationships between germline genes and diseases, and how can advanced language models be leveraged to address these challenges

Conventional relation extraction techniques face challenges in identifying complex relationships between germline genes and diseases due to the nuanced nature of genetic associations and the lack of well-defined causal links in the literature. Germline mutations exhibit varying effects on disease susceptibility, making it difficult to establish direct causal relationships between genes and diseases. Traditional methods may struggle to capture the subtle interactions and dependencies inherent in germline associations. Advanced language models, such as BERT-based models fine-tuned on gene and disease datasets, offer a more sophisticated approach to address these challenges. These models can learn intricate patterns and context-specific information from biomedical texts, enabling them to extract and classify gene-disease relationships with higher accuracy. By leveraging pre-trained language models, researchers can overcome the limitations of conventional techniques and extract more nuanced insights from germline corpora.

Given the inherent complexity of germline mutations and their impact on disease susceptibility, how can the SimpleGermKG be extended to provide personalized insights and risk assessments for individuals based on their genetic profiles

Extending SimpleGermKG to provide personalized insights and risk assessments based on genetic profiles involves integrating individual genetic data with germline gene-disease associations. By incorporating personalized genetic information, such as specific gene variants and mutations, into the knowledge graph, SimpleGermKG can offer tailored recommendations for disease prevention, diagnosis, and treatment. The graph can be enriched with data on individual genetic predispositions, family history, and lifestyle factors to generate personalized risk assessments for various diseases. Advanced analytics and machine learning algorithms can analyze this integrated data to predict an individual's susceptibility to specific diseases based on their genetic profile. By combining personalized genetic insights with germline gene-disease associations, SimpleGermKG can empower individuals and healthcare providers with valuable information for proactive health management and personalized medical interventions.