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

Huang, Ming; Lai, Po Ting; Lin, Pei Yen; You, Yu Ting; Tsai, Richard Tzong Han; Hsu, Wen Lian

doi:10.1093/bib/bbaa054

Cited by 35 publications

(14 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are methods aimed at NER that have been developing during the last years (Kaewphan et al, 2018;Korvigo et al, 2018;Hemati and Mehler, 2019;Hong and Lee, 2020;Huang et al, 2020;Kilicoglu et al, 2020). Most of them are based on algorithms for NER related either to chemicals or biological objects.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

et al. 2020

View full text Add to dashboard Cite

Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…JLNPBA (Huang et al , 2020) dataset is formed from MEDLINE by using MeSH terms “human,” “blood cells” and “transcription factors.” Two thousand abstracts have been selected from this search and hand-annotated, based on a small 48 class's taxonomic classification. Out of 48 classes, 36 were used to annotate the GENIA corpus.…”

Section: Experimentationmentioning

confidence: 99%

ABEE: automated bio entity extraction from biomedical text documents

Kumar

Sharaff

2023

DTA

View full text Add to dashboard Cite

PurposeThe purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.Design/methodology/approachIn the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.FindingsThe proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.Research limitations/implicationsAs such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.Practical implicationsAs far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.Social implicationsDuring the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.Originality/valueIn this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

show abstract

“…Information extraction systems have also been intensively researched and developed ( 9 ), allowing the automatic mining of key knowledge that helps to keep biomedical databases updated and alleviating the need for manual efforts ( 10 , 11 ). For instance, previous research efforts on information extraction have focused on identifying biomedical entities such as genes, proteins, chemical compounds ( 12 ) and clinical entities including laboratory procedures, diseases and adverse effects ( 13 , 14 ). Identification of these entities of interest in the text is commonly paired with a normalization step, where the entities are grounded to unique identifiers from standard vocabularies or databases.…”

Section: Related Workmentioning

confidence: 99%

Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics

et al. 2022

View full text Add to dashboard Cite

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available. Database URL https://github.com/bioinformatics-ua/biocreativeVII_track2

show abstract

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

Cited by 35 publications

References 41 publications

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

ABEE: automated bio entity extraction from biomedical text documents

Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics

Contact Info

Product

Resources

About