Extracting chemical–protein relations using attention-based neural networks

Liu, Sijia; Shen, Feichen; Elayavilli, Ravikumar Komandur; Wang, Yanshan; Rastegar-Mojarad, Majid; Chaudhary, Vipin; Liu, Hongfang

doi:10.1093/database/bay102

Cited by 33 publications

(24 citation statements)

References 42 publications

(36 reference statements)

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“…In the pooling layer, the attention pooling (22) is applied to map the multihead attention matrix to the sentence vector representation. The ‘softmax’ function is used in the output layer to implement the detection and classification of CPIs.…”

Section: Methodsmentioning

confidence: 99%

“…We introduce a deep neural model to extract CPIs from the literature, which includes an ELMo input layer, bidirectional long short-term memory networks (Bi-LSTMs) and a multihead attention layer. Liu et al (22) integrated attention pooling into the gated recurrent unit (GRU) model to extract CPIs. Verga et al (23) combined the multihead attention with CNNs to construct transformer model to extract the document-level biomedical relations.…”

Section: Introductionmentioning

confidence: 99%

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Chemical–protein interaction extraction via contextualized word representations and multihead attention

et al. 2019

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A rich source of chemical–protein interactions (CPIs) is locked in the exponentially growing biomedical literature. Automatic extraction of CPIs is a crucial task in biomedical natural language processing (NLP), which has great benefits for pharmacological and clinical research. Deep context representation and multihead attention are recent developments in deep learning and have shown their potential in some NLP tasks. Unlike traditional word embedding, deep context representation has the ability to generate comprehensive sentence representation based on the sentence context. The multihead attention mechanism can effectively learn the important features from different heads and emphasize the relatively important features. Integrating deep context representation and multihead attention with a neural network-based model may improve CPI extraction. We present a deep neural model for CPI extraction based on deep context representation and multihead attention. Our model mainly consists of the following three parts: a deep context representation layer, a bidirectional long short-term memory networks (Bi-LSTMs) layer and a multihead attention layer. The deep context representation is employed to provide more comprehensive feature input for Bi-LSTMs. The multihead attention can effectively emphasize the important part of the Bi-LSTMs output. We evaluated our method on the public ChemProt corpus. These experimental results show that both deep context representation and multihead attention are helpful in CPI extraction. Our method can compete with other state-of-the-art methods on ChemProt corpus.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chemical–protein interaction extraction via contextualized word representations and multihead attention

et al. 2019

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“…Efforts such as ChemProt (Krallinger et al, 2017) aim to promote research in this direction by presenting PubMed abstracts in which proteins, chemicals, and their relations are manually labeled. Liu et al, 2018 andLim andKang, 2018 developed sequential models for chemical and protein relation extraction on ChemProt, while Peng et al, 2018 used an ensemble of deep models with SVM. However, all these models depend on the named entities to be recognized beforehand.…”

Section: Related Workmentioning

confidence: 99%

Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature

Köksal

Dönmez

Özçelik

et al. 2020

Preprint

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Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an enormous scientific effort from different domains. Resulting publications formed a gigantic domain-specific collection of text in which finding studies on a biomolecule of interest is quite challenging for general purpose search engines due to terminology-rich characteristics of the publications. Here, we present Vapur, an online COVID-19 search engine specifically designed for finding related protein - chemical pairs. Vapur is empowered with a biochemically related entities-oriented inverted index in order to group studies relevant to a biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.

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“…Deep learning has been increasingly popular as these methods can outperform common machine learning methods [49]. Approaches in this field consist of using various neural network architectures, such as recurrent neural networks [50,51,52,53,54,55] and convolutional neural networks [51,54,56,57,58], to extract relationships from text. In fact approaches in this field were the winning model within the BioCreative VI shared task [41,59].…”

Section: Supervised Extractorsmentioning

confidence: 99%

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

Nicholson

Himmelstein

Greene

2019

Preprint

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Knowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

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Extracting chemical–protein relations using attention-based neural networks

Cited by 33 publications

References 42 publications

Chemical–protein interaction extraction via contextualized word representations and multihead attention

Chemical–protein interaction extraction via contextualized word representations and multihead attention

Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

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