1
|
Yan J, Chen L, Yu Y, Xu H, Xu Z, Sheng Y, Chen J. Neuroimaging-ITM: A Text Mining Pipeline Combining Deep Adversarial Learning with Interaction Based Topic Modeling for Enabling the FAIR Neuroimaging Study. Neuroinformatics 2022; 20:701-726. [PMID: 35235184 DOI: 10.1007/s12021-022-09571-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2022] [Indexed: 12/31/2022]
Abstract
Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging study. In order to avoid fragmented information, the Brain Informatics provenance model is redesigned based on NIDM (Neuroimaging Data Model) and FAIR facets. It can systematically capture the provenance requests from the FAIR neuroimaging study and then transform them into a group of text mining tasks. A neuroimaging text mining pipeline combining deep adversarial learning with interaction based topic modeling, called neuroimaging interaction topic model (Neuroimaging-ITM), is proposed to automatically extract neuroimaging provenance and identify research topics in the few-shot scenario. Finally, a group of experiments is completed by using real data from the journal PloS One. The experimental results show that Neuroimaging-ITM can systematically and accurately extract provenance information and obtain high-quality research topics from the full text of neuroimaging articles. Most of the mean F1 values of provenance extraction exceed 0.9. The topic coherence and KL (Kullback-Leibler) divergence reach 9.95 and 0.96 respectively. The results are obviously better than baseline methods.
Collapse
Affiliation(s)
- Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Yongchuan Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Hongxia Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. .,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, 100124, China. .,Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing, 100124, China.
| |
Collapse
|
2
|
Lin S, Xu Z, Sheng Y, Chen L, Chen J. AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction. Front Neurosci 2022; 15:739535. [PMID: 35321479 PMCID: PMC8936590 DOI: 10.3389/fnins.2021.739535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.
Collapse
Affiliation(s)
- Shaofu Lin
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Institute of Smart City, Beijing University of Technology, Beijing, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging (MRI) and Brain Informatics, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, China
- *Correspondence: Jianhui Chen,
| |
Collapse
|