MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.
In: Computational and structural biotechnology journal, Jg. 23 (2024-03-27), S. 1339-1347
Online
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Zugriff:
Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.
Competing Interests: None.
(© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Titel: |
MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.
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Autor/in / Beteiligte Person: | Yang, Y ; Lu, Y ; Zheng, Z ; Wu, H ; Lin, Y ; Qian, F ; Yan, W |
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Zeitschrift: | Computational and structural biotechnology journal, Jg. 23 (2024-03-27), S. 1339-1347 |
Veröffentlichung: | Amsterdam : Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology ; <i>Original Publication</i>: Gothenburg, Sweden : Research Network of Computational and Structural Biotechnology, 2024 |
Medientyp: | academicJournal |
ISSN: | 2001-0370 (print) |
DOI: | 10.1016/j.csbj.2024.03.021 |
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