scholarly journals FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining

2017 ◽  
Author(s):  
John A Bachman ◽  
Benjamin M Gyori ◽  
Peter K Sorger
Keyword(s):  
Molecular Mechanisms ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Hierarchical Organization ◽  
Event Extraction ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Protein Families ◽  
Scientific Publications ◽  
Named Entity

AbstractBackgroundFor automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “grounding.” Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers.ResultsIn a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., “AKT”) and complexes with multiple subunits (e.g.”NF-κB”). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15% to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings.ConclusionFamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

scholarly journals Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text

2021 ◽  
Vol 11 (4) ◽  
pp. 267-273
Author(s):  
Wen-Juan Hou ◽  
◽  
Bamfa Ceesay
Keyword(s):  
Text Processing ◽  
Named Entity Recognition ◽  
Event Extraction ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Automatic Extraction ◽  
Named Entity ◽  
Structured Information ◽  
Network Approaches ◽  
Form Information

Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.

scholarly journals MetaMap Lite: an evaluation of a new Java implementation of MetaMap

2017 ◽  
Vol 24 (4) ◽  
pp. 841-844 ◽  
Author(s):  
Dina Demner-Fushman ◽  
Willie J Rogers ◽  
Alan R Aronson
Keyword(s):  
Text Analysis ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Named Entity ◽  
Medical Language

Abstract MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java. On several collections of biomedical literature and clinical text, MetaMap Lite demonstrated real-time speed and precision, recall, and F1 scores comparable to or exceeding those of MetaMap and other popular biomedical text processing tools, clinical Text Analysis and Knowledge Extraction System (cTAKES) and DNorm.

scholarly journals Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

2021 ◽  
pp. 1-13
Author(s):  
Xia Li ◽  
Qinghua Wen ◽  
Zengtao Jiao ◽  
Jiangtao Zhang
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Event Extraction ◽  
Entity Recognition ◽  
Language Models ◽  
Data Sets ◽  
External Resources ◽  
Named Entity ◽  
Evaluation Task

Abstract The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets and some other additional resources for these two subtasks were provided for participators. This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results. The pre-trained language models are widely applied in this evaluation task. Data argumentation and external resources are also helpful.

scholarly journals A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73729-73740 ◽  
Author(s):  
Donghyeon Kim ◽  
Jinhyuk Lee ◽  
Chan Ho So ◽  
Hwisang Jeon ◽  
Minbyul Jeong ◽  
...  
Keyword(s):  
Text Mining ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Named Entity

scholarly journals Comparing general and specialized word embeddings for biomedical named entity recognition

2021 ◽  
Vol 7 ◽  
pp. e384
Author(s):  
Rigo E. Ramos-Vargas ◽  
Israel Román-Godínez ◽  
Sulema Torres-Ramos
Keyword(s):  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Word Embedding ◽  
The Other ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity ◽  
Word Representation ◽  
The One ◽  
Biomedical Named Entity Recognition

Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used. One common criterion for selecting a word embedding is the type of source from which it is generated; that is, general (e.g., Wikipedia, Common Crawl), or specific (e.g., biomedical literature). Using specific word embeddings for the BioNER task has been strongly recommended, considering that they have provided better coverage and semantic relationships among medical entities. To the best of our knowledge, most studies have focused on improving BioNER task performance by, on the one hand, combining several features extracted from the text (for instance, linguistic, morphological, character embedding, and word embedding itself) and, on the other, testing several state-of-the-art named entity recognition algorithms. The latter, however, do not pay great attention to the influence of the word embeddings, and do not facilitate observing their real impact on the BioNER task. For this reason, the present study evaluates three well-known NER algorithms (CRF, BiLSTM, BiLSTM-CRF) with respect to two corpora (DrugBank and MedLine) using two classic word embeddings, GloVe Common Crawl (of the general type) and Pyysalo PM + PMC (specific), as unique features. Furthermore, three contextualized word embeddings (ELMo, Pooled Flair, and Transformer) are compared in their general and specific versions. The aim is to determine whether general embeddings can perform better than specialized ones on the BioNER task. To this end, four experiments were designed. In the first, we set out to identify the combination of classic word embedding, NER algorithm, and corpus that results in the best performance. The second evaluated the effect of the size of the corpus on performance. The third assessed the semantic cohesiveness of the classic word embeddings and their correlation with respect to several gold standards; while the fourth evaluates the performance of general and specific contextualized word embeddings on the BioNER task. Results show that the classic general word embedding GloVe Common Crawl performed better in the DrugBank corpus, despite having less word coverage and a lower internal semantic relationship than the classic specific word embedding, Pyysalo PM + PMC; while in the contextualized word embeddings the best results are presented in the specific ones. We conclude, therefore, when using classic word embeddings as features on the BioNER task, the general ones could be considered a good option. On the other hand, when using contextualized word embeddings, the specific ones are the best option.

scholarly journals Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Renzo M. Rivera-Zavala ◽  
Paloma Martínez
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Pharmaceutical Chemical ◽  
Learning Models ◽  
Named Entity ◽  
Entity Identification ◽  
Biomedical Texts

Abstract Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. Methods In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. Results Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.

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