{"title":"通过结构感知图变换器和路径积分卷积预测 CircRNA 与疾病的关联性","authors":"Jinkai Wu, PengLi Lu, Wenqi Zhang","doi":"10.1016/j.ab.2024.115554","DOIUrl":null,"url":null,"abstract":"<div><p>A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.</p></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"692 ","pages":"Article 115554"},"PeriodicalIF":2.6000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution\",\"authors\":\"Jinkai Wu, PengLi Lu, Wenqi Zhang\",\"doi\":\"10.1016/j.ab.2024.115554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.</p></div>\",\"PeriodicalId\":7830,\"journal\":{\"name\":\"Analytical biochemistry\",\"volume\":\"692 \",\"pages\":\"Article 115554\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytical biochemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003269724000988\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical biochemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003269724000988","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution
A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.
期刊介绍:
The journal''s title Analytical Biochemistry: Methods in the Biological Sciences declares its broad scope: methods for the basic biological sciences that include biochemistry, molecular genetics, cell biology, proteomics, immunology, bioinformatics and wherever the frontiers of research take the field.
The emphasis is on methods from the strictly analytical to the more preparative that would include novel approaches to protein purification as well as improvements in cell and organ culture. The actual techniques are equally inclusive ranging from aptamers to zymology.
The journal has been particularly active in:
-Analytical techniques for biological molecules-
Aptamer selection and utilization-
Biosensors-
Chromatography-
Cloning, sequencing and mutagenesis-
Electrochemical methods-
Electrophoresis-
Enzyme characterization methods-
Immunological approaches-
Mass spectrometry of proteins and nucleic acids-
Metabolomics-
Nano level techniques-
Optical spectroscopy in all its forms.
The journal is reluctant to include most drug and strictly clinical studies as there are more suitable publication platforms for these types of papers.