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Mining latent relations between disease and transcription factor based on knowledge graph: a case study on Alzheimer's Disease
Hu ZY(胡正银)1,2; Dai B(代冰)1,2; Zhang Yi3; Zhu YL(朱艳玲)4; Chen WJ(陈文杰)1
2020-11
Conference Name10th Global TechMining Conference
Source Publication10th Global TechMining Conference
Issue0
Conference Date2020.11.11-13
Conference Placevirtual event
Abstract

Transcription factor (TF) is a general term for a variety of proteins or genes that regulate gene expression, which controls the activity of a gene by determining whether the gene’s DNA is transcribed into RNA. Alzheimer's Disease (AD) is a kind of neurodegenerative diseases which are associated with abnormal gene expression. However, the direct relations from literatures between TF and AD are very weak. This study aims to mining the latent relations between TF and AD by knowledge graph and semantic path analytics based on Literature-based discovery (LBD). Firstly, Subject-Predicate-Object (SPO) triples related are retrieved to construct a domain KG. Then, semantic paths with predications are extracted from KG using path traversal algorithm. After that we mine these semantic paths from four respects to reveal latent relations between TF and AD: key concepts identification, paths strength measurement and ranking, paths clustering, and emerging intermediate concepts prediction. Finally, the latent relations will be visualized and interpreted. The study is in process and more details could be presented on the conference. It will help inspire research ideas and make new discoveries to scientists.

Document Type会议论文
Identifierhttp://ir.las.ac.cn/handle/12502/11366
Collection中国科学院成都文献情报中心_信息技术部
Affiliation1.中国科学院成都文献情报中心
2.中国科学院大学经济管理学院图书情报与档案管理系
3.悉尼科技大学
4.中国科学院广州生物医药与健康研究院
First Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
Hu ZY,Dai B,Zhang Yi,et al. Mining latent relations between disease and transcription factor based on knowledge graph: a case study on Alzheimer's Disease[C],2020.
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