Text Mining and Hub Gene Network Analysis of Endometriosis

This study is aimed at systematically characterizing the endometriosis-associated genes based on text mining and at annotating the functions, pathways, and networks of endometriosis-associated hub genes. We extracted endometriosis-associated abstracts published between 1970 and 2020 from the PubMed database. A neural-named entity recognition and multitype normalization tool for biomedical text mining was used to recognize and normalize the genes and proteins embedded in the abstracts. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to annotate the functions and pathways of recognized genes. Protein-protein interaction analysis was conducted on the genes significantly cooccurring with endometriosis to identify the endometriosis-associated hub genes. A total of 433 genes were recognized as endometriosis-associated genes ( P < 0.05 ), and 154 pathways were significantly enriched ( P < 0.05 ). A network of endometriosis-associated genes with 278 gene nodes and 987 interaction links was established. The 15 proteins that interacted with 20 or more other proteins were identified as the hub proteins of the endometriosis-associated protein network. This study provides novel insights into the hub genes that play key roles in the development of endometriosis and have implications for developing targeted interventions for endometriosis.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04397-w ◽

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.

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

IEEE Access ◽

10.1109/access.2019.2920708 ◽

2019 ◽

Vol 7 ◽

pp. 73729-73740 ◽

Cited By ~ 13

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

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Tagger: BeCalm API for rapid named entity recognition

10.1101/115022 ◽

2017 ◽

Cited By ~ 2

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.

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Text mining of 15 million full-text scientific articles

10.1101/162099 ◽

2017 ◽

Cited By ~ 5

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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A BERT-Based Hybrid System for Chemical Identification and Indexing in Full-Text Articles

10.1101/2021.10.27.466183 ◽

2021 ◽

Author(s):

Arslan Erdengasileng ◽

Keqiao Li ◽

Qing Han ◽

Shubo Tian ◽

Jian Wang ◽

...

Keyword(s):

Text Mining ◽

Information Extraction ◽

Full Text ◽

Data Augmentation ◽

Named Entity Recognition ◽

Entity Recognition ◽

Chemical Identification ◽

Dictionary Matching ◽

Named Entity ◽

Chemical Named Entity Recognition

Identification and indexing of chemical compounds in full-text articles are essential steps in biomedical article categorization, information extraction, and biological text mining. BioCreative Challenge was established to evaluate methods for biological text mining and information extraction. Track 2 of BioCreative VII (summer 2021) consists of two subtasks: chemical identification and chemical indexing in full-text PubMed articles. The chemical identification subtask also includes two parts: chemical named entity recognition (NER) and chemical normalization. In this paper, we present our work on developing a hybrid pipeline for chemical named entity recognition, chemical normalization, and chemical indexing in full-text PubMed articles. Specifically, we applied BERT-based methods for chemical NER and chemical indexing, and a sieve-based dictionary matching method for chemical normalization. For subtask 1, we used PubMedBERT with data augmentation on the chemical NER task. Several chemical-MeSH dictionaries including MeSH.XML, SUPP.XML, MRCONSO.RFF, and PubTator chemical annotations are used in a specific order to get the best performance on chemical normalization. We achieved an F1 score of 0.86 and 0.7668 on chemical NER and chemical normalization, respectively. For subtask 2, we formulated it as a binary prediction problem for each individual chemical compound name. We then used a BERT-based model with engineered features and achieved a strict F1 score of 0.4825 on the test set, which is substantially higher than the median F1 score (0.3971) of all the submissions.

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Text Mining of Disease-lifestyle Associations to Explain Comorbidities in Electronic Health Registries

10.1101/168211 ◽

2017 ◽

Author(s):

Lars Juhl Jensen

Keyword(s):

Text Mining ◽

Named Entity Recognition ◽

Lifestyle Factors ◽

Entity Recognition ◽

Named Entity ◽

Danish Health ◽

Underlying Causes ◽

Electronic Health ◽

Substance Consumption ◽

Health Registry

Mining of electronic health registries can reveal vast numbers of disease correlations (from hereon referred to as comorbidities for simplicity). However, the underlying causes can be hard to identify, in part because health registries usually do not record important lifestyle factors such as diet, substance consumption, and physical activity. To address this challenge, I developed a text-mining approach that uses dictionaries of diseases and lifestyle factors for named entity recognition and subsequently for co-occurrence extraction of disease–lifestyle associations from Medline. I show that this approach is able to extract many correct associations and provide proof-of-concept that these can provide plausible explanations for comorbidities observed in Swedish and Danish health registry data.

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A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Mathematical Problems in Engineering ◽

10.1155/2015/942435 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Meijing Li ◽

Tsendsuren Munkhdalai ◽

Xiuming Yu ◽

Keun Ho Ryu

Keyword(s):

Language Processing ◽

Protein Interaction ◽

Named Entity Recognition ◽

Entity Recognition ◽

Support Vector ◽

Protein Protein Interaction ◽

Named Entity ◽

Novel Approach ◽

Interaction Extraction ◽

Parsing Tree

Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbaa054 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2219-2238 ◽

Cited By ~ 2

Author(s):

Ming-Siang Huang ◽

Po-Ting Lai ◽

Pei-Yen Lin ◽

Yu-Ting You ◽

Richard Tzong-Han Tsai ◽

...

Keyword(s):

Language Processing ◽

Protein Interaction ◽

Named Entity Recognition ◽

Paper Analysis ◽

Biomedical Literature ◽

Entity Recognition ◽

Supplementary Information ◽

Protein Protein Interaction ◽

Named Entity ◽

Biomedical Named Entity Recognition

Abstract 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: [email protected], Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: [email protected], Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

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