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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Recognition of bacteria named entity using conditional random fields in Spark
BMC systems biology, v 12(Suppl 6), pp 106-106
22 Nov 2018
PMID: 30463540
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: Microbe plays a crucial role in the functional mechanism of an ecosystem. Identification of the interactions among microbes is an important step towards understand the structure and function of microbial communities, as well as of the impact of microbes on human health and disease. Despite the importance of it, there is not a gold-standard dataset of microbial interactions currently. Traditional approaches such as growth and co-culture analysis need to be performed in the laboratory, which are time-consuming and costly. By providing predicted candidate interactions to experimental verification, computational methods are able to alleviate this problem. Mining microbial interactions from mass medical texts is one type of computational methods. Identification of the named entity of bacteria and related entities from the text is the basis for microbial relation extraction. In the previous work, a system of bacteria named entities recognition based on the dictionary and conditional random field was proposed. However, it is inefficient when dealing with large-scale text.
Results: We implemented bacteria named entity recognition on Spark platform and designed experiments for comparison to verify the correctness and validity of the proposed system. The experimental results show that it can achieve higher F-Measure on the comparison of correctness. Moreover, the predicting speed is much faster than the previous version in large-scale biomedical datasets, and the computational efficiency is improved remarkably by about 3.1 to 6.7 times.
Conclusions: The system for bacteria named entity recognition solves the inefficiency of the previous proposed system on large-scale datasets. The proposed system has good performance in accuracy and scalability.
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Details
- Title
- Recognition of bacteria named entity using conditional random fields in Spark
- Creators
- Xiaoyan Wang - Central China Normal UniversityYichuan Li - Central China Normal UniversityTingting He - Central China Normal UniversityXingpeng Jiang - Central China Normal UniversityXiaohua Hu - Central China Normal University
- Publication Details
- BMC systems biology, v 12(Suppl 6), pp 106-106
- Publisher
- Springer Nature
- Number of pages
- 7
- Grant note
- CCNU16KFY04 / Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE 2017YFC0909502 / National Key Research and Development Program of China 61532008; 61872157 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000452394900004
- Scopus ID
- 2-s2.0-85056928743
- Other Identifier
- 991019167813504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology