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 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 SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nícia Rosário-Ferreira ◽  
Victor Guimarães ◽  
Vítor S. Costa ◽  
Irina S. Moreira
Keyword(s):  
Text Mining ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Disease Similarity ◽  
Disease Associations ◽  
Named Entity Normalization ◽  
Mining Tool ◽  
Or Gene ◽  
Text Mining Tool

Abstract Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

scholarly journals Tagger: BeCalm API for rapid named entity recognition

2017 ◽  
Author(s):  
Lars Juhl Jensen
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Real Time ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
The Real ◽  
Practical Applications ◽  
Named Entity ◽  
Highly Efficient

AbstractMost BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision of recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. The new BioCreative/BeCalm TIPS task focuses purely on these. To participate in this task, I implemented a BeCalm API within the real-time tagging server also used by the Reflect and EXTRACT tools. In addition to retrieval of patent abstracts, PubMed abstracts, and Pub-Med Central open-access articles as required in the TIPS task, the BeCalm API implementation facilitates retrieval of documents from other sources specified as custom request parameters. As in earlier tests, the tagger proved to be both highly efficient and stable, being able to consistently process requests of 5000 abstracts in less than half a minute including retrieval of the document text.

scholarly journals A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Xuan Qin ◽  
Xinzhi Yao ◽  
Jingbo Xia
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Drug Use ◽  
Natural Language ◽  
Language Processing ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Related Gene ◽  
Pathway Enrichment

BACKGROUND Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation. OBJECTIVE The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect. METHODS Four biomedical text mining methods were selected to represent natural language processing methods on drug-related gene mining. Subsequently, a pathway enrichment experiment was performed by using the mined genes, and a series of inverse pathway frequency (IPF) metrics was proposed accordingly to evaluate the effect of pathway enrichment. Thereafter, 7 IPF metrics and traditional <i>P</i> value metrics were compared in simulation experiments to test the robustness of the proposed metrics. RESULTS IPF metrics were evaluated in a case study of rapamycin-related gene set. By applying the best IPF metrics in a pathway enrichment simulation test, a novel discovery of drug efficacy of rapamycin for breast cancer was replicated from the data chosen prior to the year 2000. Our findings show the effectiveness of the best IPF metric in support of knowledge discovery in new drug use. Further, the mechanism underlying the drug-disease association was visualized by Cytoscape. CONCLUSIONS The results of this study suggest the effectiveness of the proposed IPF metrics in pathway enrichment evaluation as well as its application in drug use discovery.

scholarly journals Identifying interactions between chemical entities in biomedical text

2014 ◽  
Vol 11 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Andre Lamurias ◽  
João D. Ferreira ◽  
Francisco M. Couto
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Ensemble Classifier ◽  
Entity Recognition ◽  
Support Vector ◽  
Biomedical Text ◽  
Web Tool ◽  
Named Entity ◽  
Vector Machines ◽  
Chemical Named Entity Recognition

Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

scholarly journals Text mining of 15 million full-text scientific articles

2017 ◽  
Author(s):  
David Westergaard ◽  
Hans-Henrik Stærfeldt ◽  
Christian Tønsberg ◽  
Lars Juhl Jensen ◽  
Søren Brunak
Keyword(s):  
Text Mining ◽  
Full Text ◽  
Disease Gene ◽  
Scientific Literature ◽  
Named Entity Recognition ◽  
Recognition System ◽  
Entity Recognition ◽  
Data Sets ◽  
Named Entity ◽  
Benchmark Data

AbstractAcross academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

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