Text Mining on Big and Complex Biomedical Literature

Driven by the rapidly advancing techniques and increasing interests in biology and medicine, about 2,000 to 4,000 references are added daily to MEDLINE, the US national biomedical bibliographic database. Even for a specific research topic, extracting useful and comprehensive information out of the huge literature data pool is challenging. Text mining techniques become extremely useful when dealing with the abundant biomedical information and they have been applied to various areas in the realm of biomedical research. Instead of providing a brief overview of all text mining techniques and every major biomedical text mining application, this chapter explores in-depth the microRNA profiling area and related text mining tools. As an illustrative example, one rule-based text mining system developed by the authors is discussed in detail. This chapter also includes the discussion of the challenges and potential research areas in biomedical text mining.

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Text Mining on Big and Complex Biomedical Literature

Biomedical Engineering ◽

10.4018/978-1-5225-3158-6.ch005 ◽

2018 ◽

pp. 129-154

Author(s):

Boya Xie ◽

Qin Ding ◽

Di Wu

Keyword(s):

Text Mining ◽

Biomedical Literature ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Mining System ◽

Research Areas ◽

Comprehensive Information ◽

Related Text ◽

The Us ◽

Text Mining System

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GIS: a biomedical text-mining system for gene information discovery

Bioinformatics ◽

10.1093/bioinformatics/btg369 ◽

2003 ◽

Vol 20 (1) ◽

pp. 120-121 ◽

Cited By ~ 32

Author(s):

J.-H. Chiang ◽

H.-C. Yu ◽

H.-J. Hsu

Keyword(s):

Text Mining ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Information Discovery ◽

Mining System ◽

Text Mining System ◽

Gene Information

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BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Bioinformatics ◽

10.1093/bioinformatics/btz682 ◽

2019 ◽

Cited By ~ 137

Author(s):

Jinhyuk Lee ◽

Wonjin Yoon ◽

Sungdong Kim ◽

Donghyeon Kim ◽

Sunkyu Kim ◽

...

Keyword(s):

Text Mining ◽

State Of The Art ◽

Biomedical Literature ◽

Fine Tuning ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Representation Model ◽

Score Improvement ◽

Language Representation ◽

Previous State

Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

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Best Paper Selection

Yearbook of Medical Informatics ◽

10.1055/s-0040-1701998 ◽

2020 ◽

Vol 29 (01) ◽

pp. 225-225

Keyword(s):

Text Mining ◽

Medical Record ◽

Electronic Medical Record ◽

Biomedical Literature ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Context Analysis ◽

Representation Model ◽

Language Representation

Guan J, Li R, Yu S, Zhang X. A Method for Generating Synthetic Electronic Medical Record Text. IEEE/ACM Transact on Comput Biology and Inform 2019 https://ieeexplore.ieee.org/document/8880542 Lee J, Yoon W, Kim S, Kim D, Kim S, Ho So C, Kang J. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2019;36(4):1234-40 https://academic.oup.com/bioinformatics/article/36/4/1234/5566506 Rosemblat G, Fiszman M, Shin D, Kılıçoğlu H. Towards a characterization of apparent contradictions in the biomedical literature using context analysis. J Biomed Inform 2019;98:103275 https://www.sciencedirect.com/science/article/abs/pii/S1532046419301947?via%3Dihub

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The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

BioChem ◽

10.3390/biochem1020007 ◽

2021 ◽

Vol 1 (2) ◽

pp. 60-80

Author(s):

Nícia Rosário-Ferreira ◽

Catarina Marques-Pereira ◽

Manuel Pires ◽

Daniel Ramalhão ◽

Nádia Pereira ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Text Mining ◽

Clinical Data ◽

Scientific Literature ◽

Structured Data ◽

Biomedical Literature ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Step Process

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.

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