Application of BERT to Enable Gene Classification Based on Clinical Evidence
Joint Authors
Su, Yuhan
Xiang, Hongxin
Xie, Haotian
Yu, Yong
Dong, Shiyan
Yang, Zhaogang
Zhao, Na
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment.
Based on literature research, the classification of genetic mutations continues to be done manually nowadays.
Manual classification of genetic mutations is pathologist-dependent, subjective, and time-consuming.
To improve the accuracy of clinical interpretation, scientists have proposed computational-based approaches for automatic analysis of mutations with the advent of next-generation sequencing technologies.
Nevertheless, some challenges, such as multiple classifications, the complexity of texts, redundant descriptions, and inconsistent interpretation, have limited the development of algorithms.
To overcome these difficulties, we have adapted a deep learning method named Bidirectional Encoder Representations from Transformers (BERT) to classify genetic mutations based on text evidence from an annotated database.
During the training, three challenging features such as the extreme length of texts, biased data presentation, and high repeatability were addressed.
Finally, the BERT+abstract demonstrates satisfactory results with 0.80 logarithmic loss, 0.6837 recall, and 0.705 F-measure.
It is feasible for BERT to classify the genomic mutation text within literature-based datasets.
Consequently, BERT is a practical tool for facilitating and significantly speeding up cancer research towards tumor progression, diagnosis, and the design of more precise and effective treatments.
American Psychological Association (APA)
Su, Yuhan& Xiang, Hongxin& Xie, Haotian& Yu, Yong& Dong, Shiyan& Yang, Zhaogang…[et al.]. 2020. Application of BERT to Enable Gene Classification Based on Clinical Evidence. BioMed Research International،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1134822
Modern Language Association (MLA)
Su, Yuhan…[et al.]. Application of BERT to Enable Gene Classification Based on Clinical Evidence. BioMed Research International No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1134822
American Medical Association (AMA)
Su, Yuhan& Xiang, Hongxin& Xie, Haotian& Yu, Yong& Dong, Shiyan& Yang, Zhaogang…[et al.]. Application of BERT to Enable Gene Classification Based on Clinical Evidence. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1134822
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134822