Protein Interaction Extraction Research Articles

End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies.

End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER)) and associated relations, while most RE tasks simply assume that the entities are provided upfront and end up performing relation classification. E2ERE is inherently more difficult than RE alone given the potential snowball effect of errors from NER leading to more errors in RE. A complex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI, 2017) that identifies relations between chemical compounds and genes/proteins in scientific literature. ChemProt is included in all recent biomedical natural language processing benchmarks including BLUE, BLURB, and BigBio. However, its treatment in these benchmarks and in other separate efforts is typically not end-to-end, with few exceptions. In this effort, we employ a span-based pipeline approach to produce a new state-of-the-art E2ERE performance on the ChemProt dataset, resulting in > 4% improvement in F1-score over the prior best effort. Our results indicate that a straightforward fine-grained tokenization scheme helps span-based approaches excel in E2ERE, especially with regards to handling complex named entities. Our error analysis also identifies a few key failure modes in E2ERE for ChemProt.

Retraction Note: Medical image analysis of phosphorylated protein interaction extraction algorithm based on text mining technology

Retraction Note: Medical image analysis of phosphorylated protein interaction extraction algorithm based on text mining technology

Biomedical named entity recognition and linking datasets: survey and our recent development.

Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein-protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein-protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Chemical-protein interaction extraction via Gaussian probability distribution and external biomedical knowledge.

The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. In this article, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks. Data and code are available at https://github.com/CongSun-dlut/CPI_extraction. Supplementary data are available at Bioinformatics online.

RETRACTED ARTICLE: Medical image analysis of phosphorylated protein interaction extraction algorithm based on text mining technology

Medical images have been widely used in the analysis of phosphorylated protein interactions, Medical image segmentation separates different regions of a particular meaning in an image. These regions make each region that does not intersect with each other satisfy the consistency of a particular region. In bioinformatics, measurement on protein interaction is one of the key points, which plays a very important role in understanding various biological processes and in the treatment and diagnosis of diseases. The physiological functions of organisms are mainly regulated by proteins in cells, so studying the interaction between proteins becomes the basis for understanding life activities. Using text mining methods to study protein interactions can make full use of the vast literature available to reveal potential knowledge associated with proteins and construct protein interaction networks. Therefore, this paper proposes a method for automatically extracting protein interaction pairs from the literature by studying the extraction principle of protein interactions and their interaction words. At the same time, the method of protein interaction relationship mining is developed. The research and the results obtained in this paper have certain reference value for the study of protein relations in the future.

Chemical-protein interaction extraction via contextualized word representations and multihead attention.

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.

A Deep Learning Approach With Deep Contextualized Word Representations for Chemical–Protein Interaction Extraction From Biomedical Literature

Mining interactions between chemicals and proteins/genes is of crucial relevance for clinical medicine, adverse drug effects, and pharmacological research. Although chemical-protein interactions (CPIs) can be manually extracted, this process is expensive and time-consuming. Therefore, it is of considerable significance to automatically extract CPIs from biomedical literature. Currently, the popular methods for CPI extraction are based on deep learning to avoid sophisticated handcrafted features derived from linguistic analyses. However, the performance of existing methods is usually unsatisfactory. The reasons may be that (1) traditional word-embedding methods cannot adequately model context information, and (2) it is difficult to effectively distinguish which words play critical roles in long biomedical sentences. In this study, we propose a novel Deep-contextualized Stacked Bi-LSTM model (DS-LSTM) to tackle the drawbacks of existing methods. Specifically, our model mainly consists of three components: deep contextualized word representations, the entity attention mechanism, and stacked bidirectional long short-term memory networks (Bi-LSTMs). The deep contextualized word representations are introduced to effectively model complex characteristics of word use (e.g., syntax and semantics) and the variations of these words in the context (i.e., to model polysemy), thereby generating context information. The entity attention mechanism is applied to prioritize the weights of words associated with target entities to distinguish which words play critical roles in long biomedical sentences. We evaluate our model on the CHEMPROT corpus. Our approach achieves a micro-averaged F-score of 69.44%, which is significantly higher than existing state-of-the-art methods. Experimental results show that our approach can adequately model context information, effectively distinguish which words play critical roles in long biomedical sentences and, therefore, improve the overall performance.

PPaxe: easy extraction of protein occurrence and interactions from the scientific literature.

Protein-protein interactions (PPIs) are very important to build models for understanding many biological processes. Although several databases hold many of these interactions, exploring them, selecting those relevant for a given subject and contextualizing them can be a difficult task for researchers. Extracting PPIs directly from the scientific literature can be very helpful for providing such context, as the sentences describing these interactions may give insights to researchers in helpful ways. We have developed PPaxe, a python module and a web application that allows users to extract PPIs and protein occurrence from a given set of PubMed and PubMedCentral articles. It presents the results of the analysis in different ways to help researchers export, filter and analyze the results easily. PPaxe web demo is freely available at https://compgen.bio.ub.edu/PPaxe. All the software can be downloaded from https://compgen.bio.ub.edu/PPaxe/download, including a command-line version and docker containers for an easy installation. Supplementary data are available at Bioinformatics online.

Leveraging prior knowledge for protein-protein interaction extraction with memory network.

Automatically extracting protein–protein interactions (PPIs) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein–protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-4/

An approach to improve kernel-based Protein–Protein Interaction extraction by learning from large-scale network data

Protein–Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html.

Application and evaluation of automated methods to extract brain connectivity statements from free text

Event Abstract Back to Event Application and evaluation of automated methods to extract brain connectivity statements from free text Leon French1*, Suzanne Lane1, Lydia Xu1, Cathy Kwok1, Celia Siu1, YiQi Chen1, Claudia Krebs1 and Paul Pavlidis1 1 University of British Columbia, Canada Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large scale connectivity resources. Unfortunately, connectivity findings are not formally encoded, hindering aggregation, indexing, searching, and integration. Here we describe progress in developing an automated approach to extracting connectivity reports from free text, building on our previous work on extracting and normalizing brain region mentions. We manually annotated a set of 1,377 abstracts for connectivity relations to form a “gold standard” set. We evaluated a range of methods including simple algorithms (naïve co-occurrence) as well as sophisticated machine learning algorithms adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features. Co-occurrence based methods at various thresholds achieve higher recall and equal precision to techniques that employ complex features. A shallow linguistic kernel (SLK) method recalled 50% of the sentence level connectivity statements at 70% precision by employing a limited set of lexical features. We applied the SLK and co-occurrence approaches to 12,557 abstracts from the Journal of Comparative Neurology, resulting in 28,107 predicted connectivity relationships. We compared a normalized subset of 2,688 relationships to the Brain Architecture Management System (BAMS; an established database of rat tract tracing studies). The extracted connections were connected in BAMS at a rate of 63.5%, compared to 51.1% for co-occurring brain region pairs. Outside of the rat connections in BAMS, we estimated precision of 55.3% based on a manual evaluation of 2000 predicted connectivity statements (recall was not judged). We expanded our prediction set to an additional 5797 abstracts in other journals deemed to be connectivity related by the Mscanner method. By again employing BAMS for evaluation we found this new set of abstracts have similar levels of accuracy while extracting 1430 unique relationships that were not seen in the previous corpus. By aggregating these data into a connectivity matrix, we found that precision can be increased at the cost of recall by requiring predicted connections to occur more than once across the corpus. Further analyses of the predicted connectomes is under way. Keywords: digital atlasing, neuroanatomical connectivity, computational neuroscience, evaluation of methods, brain connectivity Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: French L, Lane S, Xu L, Kwok C, Siu C, Chen Y, Krebs C and Pavlidis P (2014). Application and evaluation of automated methods to extract brain connectivity statements from free text. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00045 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Leon French, University of British Columbia, Vancouver, Canada, leonfrench@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Leon French Suzanne Lane Lydia Xu Cathy Kwok Celia Siu YiQi Chen Claudia Krebs Paul Pavlidis Google Leon French Suzanne Lane Lydia Xu Cathy Kwok Celia Siu YiQi Chen Claudia Krebs Paul Pavlidis Google Scholar Leon French Suzanne Lane Lydia Xu Cathy Kwok Celia Siu YiQi Chen Claudia Krebs Paul Pavlidis PubMed Leon French Suzanne Lane Lydia Xu Cathy Kwok Celia Siu YiQi Chen Claudia Krebs Paul Pavlidis Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

PPIExtractor: A Protein Interaction Extraction and Visualization System for Biomedical Literature

Protein-protein interactions (PPIs) play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interaction information manually. In this paper, we present a PPI extraction system, termed PPIExtractor, which automatically extracts PPIs from biomedical text and visualizes them. Given a Medline record dataset, PPIExtractor first applies Feature Coupling Generalization (FCG) to tag protein names in text, next uses the extended semantic similarity-based method to normalize them, then combines feature-based, convolution tree and graph kernels to extract PPIs, and finally visualizes the PPI network. Experimental evaluations show that PPIExtractor can achieve state-of-the-art performance on a DIP subset with respect to comparable evaluations.

PIMiner: a web tool for extraction of protein interactions from biomedical literature

Information on Protein Interactions (Pls) is valuable for biomedical research, but often lies buried in the scientific literature and cannot be readily retrieved. While much progress has been made over the years in extracting Pls from the literature using computational methods, there is a lack of free, public, user-friendly tools for the discovery of Pls. We developed an online tool for the extraction of PI relationships from PubMed-abstracts, which we name PIMiner. Protein pairs and the words that describe their interactions are reported by PIMiner so that new interactions can be easily detected within text. The interaction likelihood levels are reported too. The option to extract only specific types of interactions is also provided. The PIMiner server can be accessed through a web browser or remotely through a client's command line. PIMiner can process 50,000 PubMed abstracts in approximately 7 min and thus appears suitable for large-scale processing of biological/biomedical literature.

Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text

Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.Availability and implementation: The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText.Contact: paul@chibi.ubc.caSupplementary information: Supplementary data are available at Bioinformatics Online.

A text-mining system for extracting metabolic reactions from full-text articles.

BackgroundIncreasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway — metabolic pathways — has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein–protein interactions.ResultsWhen evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.ConclusionsWe conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

Tree kernel-based protein–protein interaction extraction from biomedical literature

There is a surge of research interest in protein–protein interaction (PPI) extraction from biomedical literature. While most of the state-of-the-art PPI extraction systems focus on dependency-based structured information, the rich structured information inherent in constituent parse trees has not been extensively explored for PPI extraction. In this paper, we propose a novel approach to tree kernel-based PPI extraction, where the tree representation generated from a constituent syntactic parser is further refined using the shortest dependency path between two proteins derived from a dependency parser. Specifically, all the constituent tree nodes associated with the nodes on the shortest dependency path are kept intact, while other nodes are removed safely to make the constituent tree concise and precise for PPI extraction. Compared with previously used constituent tree setups, our dependency-motivated constituent tree setup achieves the best results across five commonly used PPI corpora. Moreover, our tree kernel-based method outperforms other single kernel-based ones and performs comparably with some multiple kernel ones on the most commonly tested AIMed corpus.

Protein–Protein Interaction Extraction Based on Improved All-Paths Kernel

Protein–Protein Interaction Extraction Based on Improved All-Paths Kernel

Ranking support vector machine for multiple kernels output combination in protein–protein interaction extraction from biomedical literature

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.

Neighborhood hash graph kernel for protein–protein interaction extraction

Automated extraction of protein–protein interactions (PPIs) from biomedical literatures is an important topic of biomedical text mining. In this paper, we propose an approach based on neighborhood hash graph kernel for this task. In contrast to the existing graph kernel-based approaches for PPI extraction, the proposed approach not only has the capability to make use of full dependency graphs to represent the sentence structure but also effectively control the computational complexity. We evaluate the proposed approach on five publicly available PPI corpora and perform detailed comparisons with other approaches. The experimental result shows that our approach is comparable to the state-of-the-art PPI extraction system and much faster than all-path graph kernel approach on all five PPI corpora.

Multiple kernel learning in protein–protein interaction extraction from biomedical literature

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature. We present a weighted multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, graph and part-of-speech (POS) path. In particular, we extend the shortest path-enclosed tree (SPT) and dependency path tree to capture richer contextual information. Our experimental results show that the combination of SPT and dependency path tree extensions contributes to the improvement of performance by almost 0.7 percentage units in F-score and 2 percentage units in area under the receiver operating characteristics curve (AUC). Combining two or more appropriately weighed individual will further improve the performance. Both on the individual corpus and cross-corpus evaluation our combined kernel can achieve state-of-the-art performance with respect to comparable evaluations, with 64.41% F-score and 88.46% AUC on the AImed corpus. As different kernels calculate the similarity between two sentences from different aspects. Our combined kernel can reduce the risk of missing important features. More specifically, we use a weighted linear combination of individual kernels instead of assigning the same weight to each individual kernel, thus allowing the introduction of each kernel to incrementally contribute to the performance improvement. In addition, SPT and dependency path tree extensions can improve the performance by including richer context information.