Biomedical relation extraction via knowledge-enhanced reading comprehension

Jing, Chen; Hu, Baotian; Peng, Weihua; Chen, Qingcai; Tang, Buzhou

doi:10.1186/s12859-021-04534-5

Cited by 24 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also demonstrated the crucial role of syntax in transfer learning [ 27 ]. Biotian et al [ 28 ] reported another similar study. They revealed that open-domain reading comprehension data and knowledge representation could help to improve biomedical relation extraction.…”

Section: Related Workmentioning

confidence: 76%

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method

2023

View full text Add to dashboard Cite

Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible method of extracting associations of SNP-phenotype from content considers their degree of certainty. In this paper, several machine learning methods were developed to extract ranked SNP-phenotype associations from biomedical abstracts and then were compared to each other. In addition, shallow machine learning methods, including random forest, logistic regression, and decision tree and two kernel-based methods like subtree and local context, a rule-based and a deep CNN-LSTM-based and two BERT-based methods were developed in this study to extract associations. Furthermore, the experiments indicated that although the used linguist features could be employed to implement a superior association extraction method outperforming the kernel-based counterparts, the used deep learning and BERT-based methods exhibited the best performance. However, the used PubMedBERT-LSTM outperformed the other developed methods among the used methods. Moreover, similar experiments were conducted to estimate the degree of certainty of the extracted association, which can be used to assess the strength of the reported association. The experiments revealed that our proposed PubMedBERT–CNN-LSTM method outperformed the sophisticated methods on the task.

show abstract

Section: Related Workmentioning

confidence: 76%

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method

2023

View full text Add to dashboard Cite

show abstract

“…[ 19 , 63 ]. CTD KB 9 has been extensively used in projects where the association between biomedical entities plays an important role [ 64 , 65 ]. There are 11,622 genes which are common between the CTD KB and the experimental gene expression data set so we use only these.…”

Section: Knowledge Basementioning

confidence: 99%

Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base

Verma,

Rebholz-Schuhmann,

Madden

2024

BMC Bioinformatics

View full text Add to dashboard Cite

Background Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients’ temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems. Results We propose two new algorithms, LOADDx and SCADDx, to combine a patient’s gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average. Conclusions We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

show abstract

“…It achieved better results compared with other document-level relation extraction owing to capturing more complete semantic information of entities. Meanwhile, Chen et al 16 leveraged reading comprehension and prior knowledge for biomedical relation extraction. Their methods achieved better performance compared with other methods because of the novel framework and supplementary prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Question-Answer framework with multi-GRU to detect Adverse Drug Reaction on social media

Luo,

Yang

2024

Preprint

View full text Add to dashboard Cite

Adverse Drug Reactions (ADRs) stand out as a pressing challenge in public health and a critical aspect of drug discovery. The dilemma arises from the inherent impossibility of conducting a comprehensive evaluation of a drug before its market release, constrained by the limitations in scale and duration of clinical trials. Therefore, the post-marketing detection of ADRs in a timely and accurate manner becomes imperative. Adding to the complexity, a multitude of tweets harbor concealed information about adverse drug reactions, creating difficulties due to their concise, sporadic, and noisy content. To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. The Von Mises–Fisher distribution (vMF distribution) is applied to derive keyword vectors through tweet sampling. An attention mecha-nism is employed to enhance the interaction between these keyword vectors and the word sequences within tweets. The credibility of word sequences is systematically evaluated based on the reliability of answer factors. To address concerns related to background information and training speed, we propose a quality assurance mechanism utilizing a GRU network due to its straightforward structure and efficient training capabilities. As a result of the training process, word sequences are mapped to a low-latitude vector space, generating corresponding answers. Experimental results obtained from two Twitter ADR datasets affirm that our Question-Answer Mechanism, leveraging multi-GRU architecture, significantly improves the accuracy of ADR detection in tweets. Our method achieved F1-scores of 81.3% and 73.3% on the two datasets, respectively, while consistently maintaining a higher recall.

show abstract

Biomedical relation extraction via knowledge-enhanced reading comprehension

Cited by 24 publications

References 26 publications

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method

Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base

Exploiting Question-Answer framework with multi-GRU to detect Adverse Drug Reaction on social media

Contact Info

Product

Resources

About