PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection
文献类型：期刊论文
| 作者 | Ma, Ke2,3; Li, Jiawei4; Zhao, Mengyuan3; Zamit, Ibrahim1; Lin, Bin5; Guo, Fei6; Tang, Jijun3 |
| 刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 21期号:3页码:480-491 |
| 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 |
| ISSN号 | 1545-5963 |
| DOI | 10.1109/TCBB.2024.3374430 |
| 英文摘要 | 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. |
| 资助项目 | National Key R&D Program of China |
| WOS研究方向 | Biochemistry & Molecular Biology ; Computer Science ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:001240049300011 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39637]  |
| 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.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China 2.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Fac Comp Sci & Control Engn, Shenzhen 518055, Peoples R China 4.Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin 300350, 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. |
入库方式： OAI收割
来源：计算技术研究所
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