PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection

doi:10.1109/TCBB.2024.3374430

	PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection
	Ma, Ke 1,2; Li, Jiawei 3; Zhao, Mengyuan 2; Zamit, Ibrahim 4; Lin, Bin 5; Guo, Fei 6; Tang, Jijun 2
	2024-05-01
发表期刊	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN	1545-5963
卷号	21 期号:3 页码:480-491
摘要	Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for understanding cellular processes and analyzing disease molecular mechanisms. Computational approaches can be challenging to employ when attempting to predict potential interactions between TFs and target genes. In this paper, we present a novel graph model (PPRTGI) for detecting TF-target gene interactions using DNA sequence features. Feature representations of TFs and target genes are extracted from sequence embeddings and biological associations. Then, by combining the aggregated node feature with graph structure, PPRTGI uses a graph neural network with personalized PageRank to learn interaction patterns. Finally, a bilinear decoder is applied to predict interaction scores between TF and target gene nodes. We designed experiments on six datasets from different species. The experimental results show that PPRTGI is effective in regulatory interaction inference, with our proposed model achieving an area under receiver operating characteristic score of 93.87% and an area under precision-recall curves score of 88.79% on the human dataset. This paper proposes a new method for predicting TF-target gene interactions, which provides new insights into modeling molecular networks and can thus be used to gain a better understanding of complex biological systems.
关键词	DNA Proteins Organisms Biology Proteomics Gene expression Feature extraction Transcription factor TF-target gene interactions DNA sequence graph neural network personalized PageRank
DOI	10.1109/TCBB.2024.3374430
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:001240049300011
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39638
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Guo, Fei; Tang, Jijun
作者单位	1.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Fac Comp Sci & Control Engn, Shenzhen 518055, Peoples R China 3.Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China 4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China 5.Melax Technol Inc, Houston, TX 77030 USA 6.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
推荐引用方式 GB/T 7714	Ma, Ke,Li, Jiawei,Zhao, Mengyuan,et al. PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2024,21(3):480-491.
APA	Ma, Ke.,Li, Jiawei.,Zhao, Mengyuan.,Zamit, Ibrahim.,Lin, Bin.,...&Tang, Jijun.(2024).PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,21(3),480-491.
MLA	Ma, Ke,et al."PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 21.3(2024):480-491.