Chemical-disease Relation Research Articles

Inference of Biomedical Relations Among Chemicals, Genes, Diseases, and Symptoms Using Knowledge Representation Learning

Knowledge representation learning represents entities and relations of knowledge graph in a continuous low-dimensional semantic space. Recently, various representation learning models have successfully been developed to infer novel relations in general-purpose knowledge bases such as FreeBase and WordNet. However, few studies have used such models for biomedical data for inferring useful relations among biomedical entities such as genes, chemicals, diseases, and symptoms. This study aimed to compare the potential of representation learning models in extracting biomedical relations by using four different types of representation learning models, viz., TransE, PTransE, TransR, and TransH. For training and evaluating the models, we collected and utilized manually curated data from public databases, including relations among chemicals, genes, diseases, and symptoms. Overall, TransE, the most efficient translation-based monolingual knowledge graph embedding model, displayed the best performance with a higher learning speed for large-scale biomedical data. Using TransE, we inferred new relations among chemicals, genes, diseases, and symptoms, and evaluated the reliability of these inferred relations. Furthermore, TransE outperformed an existing statistical method used in the Comparative Toxicogenomics Database for inferring new chemical-disease relations. Together, the present results show that the representation learning model is useful for inferring new biological data from numerous existing biomedical data.

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.

The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.

RelSCAN - A system for extracting chemical-induced disease relation from biomedical literature.

This paper proposes an effective and robust approach for Chemical-Induced Disease (CID) relation extraction from PubMed articles. The study was performed on the Chemical Disease Relation (CDR) task of BioCreative V track-3 corpus. The proposed system, named relSCAN, is an efficient CID relation extraction system with two phases to classify relation instances from the Co-occurrence and Non-Co-occurrence mention levels. We describe the case of chemical and disease mentions that occur in the same sentence as 'Co-occurrence', or as 'Non-Co-occurrence' otherwise. In the first phase, the relation instances are constructed on both mention levels. In the second phase, we employ a hybrid feature set to classify the relation instances at both of these mention levels using the combination of two Machine Learning (ML) classifiers (Support Vector Machine (SVM) and J48 Decision tree). This system is entirely corpus dependent and does not rely on information from external resources in order to boost its performance. We achieved good results, which are comparable with the other state-of-the-art CID relation extraction systems on the BioCreative V corpus. Furthermore, our system achieves the best performance on the Non-Co-occurrence mention level.

A document level neural model integrated domain knowledge for chemical-induced disease relations

BackgroundThe effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although existing CID systems explored the utilization for knowledge bases, the effects of different knowledge on the identification of a special CID haven’t been distinguished by these systems. Moreover, systems based on neural network only constructed sentence or mention level models.ResultsIn this work, we proposed an effective document level neural model integrated domain knowledge to extract CID relations from biomedical articles. Basic semantic information of an article with respect to a special CID candidate pair was learned from the document level sub-network module. Furthermore, knowledge attention depending on the representation of the article was proposed to distinguish the influences of different knowledge on the special CID pair and then the final representation of knowledge was formed by aggregating weighed knowledge. Finally, the integrated representations of texts and knowledge were passed to a softmax classifier to perform the CID recognition. Experimental results on the chemical-disease relation corpus proposed by BioCreative V show that our proposed system integrated knowledge achieves a good overall performance compared with other state-of-the-art systems.ConclusionsExperimental analyses demonstrate that the introduced attention mechanism on domain knowledge plays a significant role in distinguishing influences of different knowledge on the judgment for a special CID relation.

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.

D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information.

Recognition of biomedical named entities in the textual literature is a highly challenging research topic with great interest, playing as the prerequisite for extracting huge amount of high-valued biomedical knowledge deposited in unstructured text and transforming them into well-structured formats. Long Short-Term Memory (LSTM) networks have recently been employed in various biomedical named entity recognition (NER) models with great success. They, however, often did not take advantages of all useful linguistic information and still have many aspects to be further improved for better performance. We propose D3NER, a novel biomedical named entity recognition (NER) model using conditional random fields and bidirectional long short-term memory improved with fine-tuned embeddings of various linguistic information. D3NER is thoroughly compared with seven very recent state-of-the-art NER models, of which two are even joint models with named entity normalization (NEN), which was proven to bring performance improvements to NER. Experimental results on benchmark datasets, i.e. the BioCreative V Chemical Disease Relation (BC5 CDR), the NCBI Disease and the FSU-PRGE gene/protein corpus, demonstrate the out-performance and stability of D3NER over all compared models for chemical, gene/protein NER and over all models (without NEN jointed, as D3NER) for disease NER, in almost all cases. On the BC5 CDR corpus, D3NER achieves F1of93.14and84.68% for the chemical and disease NER, respectively; while on the NCBI Disease corpus, its F1 for the disease NER is 84.41%. Its F1 for the gene/protein NER on FSU-PRGE is 87.62%. Data and source code are available at: https://github.com/aidantee/D3NER. Supplementary data are available at Bioinformatics online.

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.

LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.

Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task–BioCreative VI, to capture chemical–protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical–Disease Relation corpus and Protein–Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.

An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition.

In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). Data and code are available at https://github.com/lingluodlut/Att-ChemdNER. yangzh@dlut.edu.cn or wangleibihami@gmail.com. Supplementary data are available at Bioinformatics online.

A transition-based joint model for disease named entity recognition and normalization.

Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this. We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies. We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches. Data and code are available at https://github.com/louyinxia/jointRN. dhji@whu.edu.cn.

Chemical-induced disease relation extraction via convolutional neural network.

This article describes our work on the BioCreative-V chemical–disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/

Improving chemical disease relation extraction with rich features and weakly labeled data

BackgroundDue to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations.ResultsWe propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data.ConclusionsOur text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system’s performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a ‘silver’ CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID–The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530).

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/

Extraction of chemical-induced diseases using prior knowledge and textual information.

We describe our approach to the chemical–disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID). RELigator is available as a web service at http://biosemantics.org/index.php/software/religator.

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/.

CD-REST: a system for extracting chemical-induced disease relation in literature.

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug–disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed from http://clinicalnlptool.com/cdr. The online CD-REST demonstration system is available at http://clinicalnlptool.com/cdr/cdr.html.Database URL: http://clinicalnlptool.com/cdr; http://clinicalnlptool.com/cdr/cdr.html

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.

Argo: enabling the development of bespoke workflows and services for disease annotation.

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo’s capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V’s User Interactive Track (IAT), we demonstrated and evaluated Argo’s suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track’s top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo’s support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo’s potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk