MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Yang Yang; Yuwei Lu; Zixuan Zheng; Hao Wu; Yuxin Lin; Fuliang Qian; Wenying Yan

doi:10.1016/j.csbj.2024.03.021

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Comput Struct Biotechnol J. 2024 Mar 27:23:1339-1347. doi: 10.1016/j.csbj.2024.03.021. eCollection 2024 Dec.

Authors

Yang Yang^{1

2}, Yuwei Lu², Zixuan Zheng², Hao Wu³, Yuxin Lin^{4

5}, Fuliang Qian^{4

6

7}, Wenying Yan^{3

4

7}

Affiliations

¹ Computing Science and Artificial Intelligence College, Suzhou City University, Suzhou 215004, China.
² School of Computer Science & Technology, Soochow University, Suzhou 215000, China.
³ Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
⁴ Center for Systems Biology, Soochow University, Suzhou 215123, China.
⁵ Department of Urology, the First Affiliated Hospital of Soochow University, Suzhou 215000, China.
⁶ Medical Center of Soochow University, Suzhou 215123, China.
⁷ Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China.

Abstract

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/.

Keywords: Drug discovery; Entity normalization; Entity recognition; Gastric cancer; Relation classification.