A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

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Mining microbe–disease interactions from literature via a transfer learning model

BMC Bioinformatics ◽

10.1186/s12859-021-04346-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Chengkun Wu ◽

Xinyi Xiao ◽

Canqun Yang ◽

JinXiang Chen ◽

Jiacai Yi ◽

...

Keyword(s):

Text Mining ◽

Large Scale ◽

Named Entity Recognition ◽

Learning Model ◽

Biomedical Literature ◽

Fine Tuning ◽

Entity Recognition ◽

Interaction Extraction ◽

Biomedical Texts ◽

Data Browsing

Abstract Background Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. Results Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading. Conclusions Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average $$F_1$$ F 1 -score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/

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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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OSIRISv1.2: A named entity recognition system for sequence variants of genes in biomedical literature

BMC Bioinformatics ◽

10.1186/1471-2105-9-84 ◽

2008 ◽

Vol 9 (1) ◽

Cited By ~ 19

Author(s):

Laura I Furlong ◽

Holger Dach ◽

Martin Hofmann-Apitius ◽

Ferran Sanz

Keyword(s):

Named Entity Recognition ◽

Recognition System ◽

Biomedical Literature ◽

Entity Recognition ◽

Sequence Variants ◽

Named Entity

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DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

10.1101/2021.12.07.471296 ◽

2021 ◽

Author(s):

Dhouha Grissa ◽

Alexander Junge ◽

Tudor I Oprea ◽

Lars Juhl Jensen

Keyword(s):

Text Mining ◽

Disease Gene ◽

Association Studies ◽

Named Entity Recognition ◽

Biomedical Literature ◽

Entity Recognition ◽

Genome Wide Association Studies ◽

Comprehensive Overview ◽

Genome Wide ◽

Gene Associations

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

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Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Journal of Healthcare Engineering ◽

10.1155/2018/4302425 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 35

Author(s):

Wencheng Sun ◽

Zhiping Cai ◽

Yangyang Li ◽

Fang Liu ◽

Shengqun Fang ◽

...

Keyword(s):

Data Mining ◽

Text Mining ◽

Large Scale ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Processing Technologies ◽

Research Issues ◽

Depth Study ◽

Integration Data

Currently, medical institutes generally use EMR to record patient’s condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.

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

10.1101/225698 ◽

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.

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A real time Named Entity Recognition system for Arabic text mining

Language Resources and Evaluation ◽

10.1007/s10579-011-9146-z ◽

2011 ◽

Vol 46 (4) ◽

pp. 543-563 ◽

Cited By ~ 11

Author(s):

Harith Al-Jumaily ◽

Paloma Martínez ◽

José L. Martínez-Fernández ◽

Erik Van der Goot

Keyword(s):

Text Mining ◽

Real Time ◽

Named Entity Recognition ◽

Recognition System ◽

Entity Recognition ◽

Arabic Text ◽

Named Entity

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Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning (Preprint)

10.2196/preprints.12159 ◽

2018 ◽

Author(s):

Fei Li ◽

Weisong Liu ◽

Hong Yu

Keyword(s):

Deep Learning ◽

Adverse Drug Events ◽

Named Entity Recognition ◽

Relation Extraction ◽

Learning Model ◽

Entity Recognition ◽

Health Record ◽

Named Entity ◽

Related Information ◽

Deep Learning Model

BACKGROUND Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. OBJECTIVE We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps—named entity recognition and relation extraction—our second objective was to improve the deep learning model using multi-task learning between the two steps. METHODS We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. RESULTS Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance. CONCLUSIONS Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.

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