Biomedical research is expanding rapidly, leading to an overwhelming amount of published literature. To effectively utilize this knowledge, it needs to be organized and validated. Knowledge graphs offer a framework for representing semantic knowledge from literature by extracting relationships between biomedical entities and normalizing them. This paper compares rule-based and machine learning-based methods for scalable relationship extraction from biomedical literature, including traditional machine learning methods (Naive Bayes, Random Forests) and modern deep learning transformers (DistilBERT, PubMedBERT, T5, SciFive). The study examines the resilience of these methods to unbalanced and small datasets. Experimental results show that transformer-based models perform well on both small and unbalanced datasets. The PubMedBERT-based model fine-tuned on balanced data achieves the highest F1-score of 0.92, while the DistilBERT-based model follows closely with an F1-score of 0.89. BERT-based models outperform T5-based generative models in this context. The introduction highlights the exponential growth of biomedical literature, with over 950,000 articles added to Medline in 2020 alone. Researchers struggle to cope with this volume of information and require tools to find relevant articles and validate claims and hypotheses. Information retrieval approaches have been developed for bio-medicine such as PubMed and Quertle but they only provide a list of relevant articles without validating hypotheses or claims. To validate hypotheses or claims researchers need to read through significant amounts of literature manually; however these hypotheses can often be summarized as relationships between concepts in simple sentences (e.g., "Aspirin treats pain"). Extracting these named relationships from biomedical literature can contribute to building a large knowledge graph where entities are connected through various relationships. The future work section discusses the long term task of creating a comprehensive biomedical knowledge graph for target identification indication expansion and drug discovery.
- Biomedical research is expanding rapidly
- Knowledge graphs offer a framework for organizing and validating biomedical knowledge from literature
- Rule-based and machine learning-based methods are compared for relationship extraction from biomedical literature
- Transformer-based models perform well on small and unbalanced datasets
- PubMedBERT-based model achieves the highest F1-score of 0.92, followed closely by DistilBERT with an F1-score of 0.89
- BERT-based models outperform T5-based generative models in this context
- Researchers struggle to cope with the volume of biomedical literature and need tools to find relevant articles and validate claims
- Information retrieval approaches like PubMed and Quertle provide a list of relevant articles but do not validate hypotheses or claims
- Extracting named relationships from biomedical literature can contribute to building a large knowledge graph connecting entities through various relationships
- Future work involves creating a comprehensive biomedical knowledge graph for target identification, indication expansion, and drug discovery.
Biomedical research is growing quickly. This means that scientists are learning more about how our bodies work and finding new ways to help people stay healthy. Knowledge graphs are like big maps that organize and check what scientists have learned from books and articles. Scientists use different methods, like rules and machines, to find important information in these books and articles. Some computer programs called Transformer-based models are really good at finding information from small or unbalanced groups of books and articles. One program called PubMedBERT is the best at finding important information, followed closely by another program called DistilBERT. These programs are better than other programs that try to create new ideas based on what they read. Scientists need tools to help them find the right books and articles for their research, but also to make sure the things they learn are true. One way they do this is by making a big map of all the things they learn, connecting them together like puzzle pieces. In the future, scientists want to make an even bigger map of everything they know about medicine so they can find new treatments for diseases."
Definitions
- Biomedical research: The study of how our bodies work and finding new ways to help people stay healthy.
- Knowledge graphs: Big maps that organize and check what scientists have learned from books and articles.
- Rule-based methods: Using specific rules or instructions to find important information in books and articles.
- Machine learning-based methods: Using computers or machines to find important information in books and articles.
- Transformer-based models
Understanding the Growing Need for Knowledge Graphs in Biomedical Research
Biomedical research is growing at an exponential rate, with over 950,000 articles added to Medline in 2020 alone. This overwhelming amount of published literature makes it difficult for researchers to effectively utilize this knowledge. To address this challenge, a framework is needed to organize and validate the information from biomedical literature. Knowledge graphs offer a promising solution by extracting relationships between biomedical entities and normalizing them into a structured format.
Comparing Rule-Based and Machine Learning-Based Methods for Relationship Extraction
This paper compares rule-based and machine learning-based methods for scalable relationship extraction from biomedical literature. Traditional machine learning methods such as Naive Bayes and Random Forests are compared against modern deep learning transformers like DistilBERT, PubMedBERT, T5, SciFive. The study examines the resilience of these methods to unbalanced and small datasets.
Experimental Results
The experimental results show that transformer-based models perform well on both small and unbalanced datasets. The PubMedBERT-based model fine-tuned on balanced data achieves the highest F1-score of 0.92 while the DistilBERT based model follows closely with an F1 score of 0.89. BERT based models outperform T5 based generative models in this context which shows that transformer based approaches are more suitable than traditional ML approaches when dealing with unbalanced or small datasets in biomedical research applications..
Applications of Knowledge Graphs in Biomedical Research
The introduction highlights the need for tools to find relevant articles quickly without having to manually read through significant amounts of literature; however these hypotheses can often be summarized as relationships between concepts in simple sentences (e.g., "Aspirin treats pain"). Extracting these named relationships from biomedical literature can contribute to building a large knowledge graph where entities are connected through various relationships which can then be used for target identification indication expansion and drug discovery among other applications..
Future Work
The future work section discusses the long term task of creating a comprehensive biomedical knowledge graph which could enable automated hypothesis validation using natural language processing (NLP) techniques such as question answering systems or summarization algorithms applied on top of existing knowledge graphs..
In conclusion, this paper provides valuable insights into how rule-based and machine learning-based methods can be used together to extract meaningful relationships from biomedical literature efficiently while also being resilient against unbalanced or small datasets . Transformer based models have been shown to outperform traditional ML approaches making them more suitable when dealing with such challenges . Finally , further research should focus on creating comprehensive knowledge graphs that could enable automated hypothesis validation using NLP techniques applied on top of existing knowledge graphs .
