Chemical-disease Relation Extraction Research Articles

MMR: A Multi-view Merge Representation model for Chemical-Disease relation extraction

Chemical-Disease relation (CDR) extraction aims to identify the semantic relations between chemical and disease entities in the unstructured biomedical document, which provides a basis for downstream tasks such as clinical medical diagnosis and drug discovery. Compared with general domain relation extraction, it needs a more effective representation of the whole document due to the specialized nature of texts in the biomedical domain, including the biomedical entity and entity-pair representation. In this paper, we propose a novel Multi-view Merge Representation (MMR) model to thoroughly capture entity and entity-pair representation of the document. First, we utilize prior knowledge and a pre-trained transformer encoder to capture entity semantic representation. Then we employ the U-Net layer and Graph Convolution Network layer to capture global entity-pair representation. Finally, we get a specific merged representation for each entity pair to be classified. We evaluate our model on the CDR dataset published by the BioCreative-V community and achieve a state-of-the-art result.

Knowledge Guided Attention and Graph Convolutional Networks for Chemical-Disease Relation Extraction.

The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of combining deep learning models with prior knowledge is worthy of our study. In this paper, we propose a new model called Knowledge Guided Attention and Graph Convolutional Networks (KGAGN) for CDR extraction. First, to make full advantage of domain knowledge, we train entity embedding as a feature representation of input sequence, and relation embedding to capture weighted contextual information further through the attention mechanism. Then, to make full advantage of syntactic dependency information in cross-sentence CDR extraction, we construct document-level syntactic dependency graphs and encode them using a graph convolution network (GCN). Finally, the chemical-induced disease (CID) relation is extracted by using weighted context features and long-range dependency features both of which contain additional knowledge information We evaluated our model on the CDR dataset published by the BioCreative-V community and achieves an F1-score of 73.3%, surpassing other state-of-the-art methods. the code implemented by PyTorch 1.7.0 deep learning library can be downloaded from Github: https://github.com/sunyi123/cdr.

Chemical disease relation extraction through the combination of multiple mention levels: RelSCAN+

Chemical-induced disease (CID) relation extraction has been pivotal in the understanding of biological processes. A CID relation between a chemical and disease entity may be extracted either from a single sentence or from two or more adjacent sentences. We use `intrasentence level' to refer to the mention of the desired entities in the same sentence and `intersentence level? to refer to the mention of these entities in two or more adjacent sentences. This study proposes a three-phase architecture for extracting CID relations from biomedical literature by considering both sentence levels and additionally the combination of these two sentence levels which we describe as the 'joint level'. In phase 1, we construct relation instances at the intra- and intersentence levels which are subsequently combined to form the joint level. In phase 2, we extracted features specifically for an individual relation instance at the three levels. At each of these levels, we trained three classifier models that consist of the combination of two classifiers. We used the training dataset for training and later classified the CID relation instances using the test dataset. Phase 3 consists of two steps; in step 1, the classifier outputs from both the intra- and intersentence levels are combined and in step 2, the results from step 1 are combined with the results from the classifier trained at joint level using a prediction probability-based voting algorithm to determine the final result. Using the BioCreative V corpus for validation, we obtain results that outperform all the state-of-the-art systems for CID relation extraction on the standard chemical-disease relation corpus.

Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation.

BackgroundAutomatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately.ObjectiveIn this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task.MethodsTo improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding.ResultsWe evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively.ConclusionsThe GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Knowledge-guided convolutional networks for chemical-disease relation extraction

BackgroundAutomatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying.ResultsThis paper proposes a novel model called “Knowledge-guided Convolutional Networks (KCN)” to leverage prior knowledge for CDR extraction. The proposed model first learns knowledge representations including entity embeddings and relation embeddings from KBs. Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions. After that, relation embeddings are employed to further capture the weighted context features by a shared attention pooling. Finally, the weighted context features containing additional knowledge information are used for CDR extraction. Experiments on the BioCreative V CDR dataset show that the proposed KCN achieves 71.28% F1-score, which outperforms most of the state-of-the-art systems.ConclusionsThis paper proposes a novel CDR extraction model KCN to make full use of prior knowledge. Experimental results demonstrate that KCN could effectively integrate prior knowledge and contexts for the performance improvement.

Chemical-induced disease relation extraction with dependency information and prior knowledge

Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have been built. KBs are important resources for biomedical relation extraction. However, previous research pays little attention to prior knowledge. In addition, the dependency tree contains important syntactic and semantic information, which helps to improve relation extraction. So how to effectively use it is also worth studying. In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags. Then the convolution operations are performed on the SDP to produce deep semantic dependency features. After that, an attention mechanism is employed to learn the importance/weight of each semantic dependency vector related to knowledge representations learned from KBs. Finally, in order to combine dependency information and prior knowledge, the concatenation of weighted semantic dependency representations and knowledge representations is fed to the softmax layer for classification. Experiments on the BioCreative V CDR dataset show that our method achieves comparable performance with the state-of-the-art systems, and both dependency information and prior knowledge play important roles in CDR extraction task.

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction.

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.

Exploiting graph kernels for high performance biomedical relation extraction

BackgroundRelation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence.In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences.ResultsOur system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures.ConclusionsWe demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task.

Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/

A knowledge-poor approach to chemical-disease relation extraction.

The article describes a knowledge-poor approach to the task of extracting Chemical-Disease Relations from PubMed abstracts. A first version of the approach was applied during the participation in the BioCreative V track 3, both in Disease Named Entity Recognition and Normalization (DNER) and in Chemical-induced diseases (CID) relation extraction. For both tasks, we have adopted a general-purpose approach based on machine learning techniques integrated with a limited number of domain-specific knowledge resources and using freely available tools for preprocessing data. Crucially, the system only uses the data sets provided by the organizers. The aim is to design an easily portable approach with a limited need of domain-specific knowledge resources. In the participation in the BioCreative V task, we ranked 5 out of 16 in DNER, and 7 out of 18 in CID. In this article, we present our follow-up study in particular on CID by performing further experiments, extending our approach and improving the performance.

Exploiting syntactic and semantics information for chemical-disease relation extraction.

Identifying chemical–disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This article proposes a novel syntactic and semantic information exploitation method for CDR extraction. The proposed method consists of a feature-based model, a tree kernel-based model and a neural network model. The feature-based model exploits lexical features, the tree kernel-based model captures syntactic structure features, and the neural network model generates semantic representations. The motivation of our method is to fully utilize the nice properties of the three models to explore diverse information for CDR extraction. Experiments on the BioCreative V CDR dataset show that the three models are all effective for CDR extraction, and their combination could further improve extraction performance. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.