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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI yi pan School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI yi pan School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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Metabolite-Disease Association Prediction Algorithm Combining DeepWalk and Random Forest

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Tsinghua Science and Technology 2022年第1期27卷 58-67页

作者： Jiaojiao Tie Xiujuan Lei yi pan the School of Computer Science Shaanxi Normal UniversityXi'an 710119China the the Department of Computer Science Georgia State UniversityAtlanta.GA 30302-3994USA

Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating ***,traditional biometric methods are time consuming and ***,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease ***,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite ***,DeepWalk is used to extract metabolite features based on the network of metabolite-gene ***,a random forest algorithm is employed to infer metabolite-disease *** experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold *** studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.

关键词： Deep Walk random forest metabolite-disease associations molecular fingerprint similarity of metabolites

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan pan yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Guest Editorial: Special Issue on Bioinformatics and Computational Biology

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Tsinghua Science and Technology 2013年第5期18卷 429-430页

作者： yi pan Department of Computer Science Georgia State University

Bioinformatics and computational biology research is fundamental to our understanding of complex biological systems,impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical *** is one of the fastest developing research fields in the last two *** throughput biological data that are used to provide information at molecular and genetic level are rapidly *** all research problems in biological and medical sciences nowadays are computationally *** techniques

关键词： Cytoscape Guest Editorial Special Issue on Bioinformatics and Computational Biology

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Visible-light-mediated tungsten-catalyzed C-H amination of unactivated alkanes with nitroarenes

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Science China Chemistry 2022年第4期65卷 678-685页

作者： Qing Wang Shengyang Ni Xiaochen Wang yi Wang yi pan State Key Laboratory of Coordination Chemistry Jiangsu Key Laboratory of Advanced Organic MaterialsSchool of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210023China

Alkylamines are important motifs in pharmaceutical and material *** existing reports of C-H amination are limited to ammonia,diazo and azide nitrogen *** work describes a rapid construction of C-N bonds from accessible nitroarene and alkane feedstock under decatungstate catalysis.A variety of C-H precursors including gaseous,linear,cyclic and benzylic hydrocarbons could adopt this protocol to afford the corresponding alkylamines in high efficiency.

关键词： amination photoredox light alkane nitrobenzene

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Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure

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Journal of Computer Science & Technology 2021年第2期36卷 288-298页

作者： Xiu-Juan Lei Chen Bian yi pan School of Computer Science Shaanxi Normal UniversityXi'an 710119China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science Georgia State UniversityAtlantaGA 30302U.S.A

Circular RNAs(circRNAs)are RNAs with a special closed loop structure,which play important roles in tumors and other *** to the time consumption of biological experiments,computational methods for predicting associations between circRNAs and diseases become a better *** the limited number of verified circRNA-disease associations into account,we propose a method named CDWBMS,which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease *** adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and *** terms of leave-one-out-cross-validation(LOOCV),10-fold cross-validation and 5-fold cross-validation,CDWBMS yields the area under the receiver operating characteristic curve(AUC)values of 0.9216,0.9172 and 0.9005,***,case studies show that CDWBMS can predict unknow circRNA-disease *** conclusion,CDWBMS is an effective method for exploring disease-related circRNAs.

关键词： circular RNA(circRNA) circRNA-disease association meta-structure heterogeneous network

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of Computer Science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU yi pan School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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TW-Co-MFC:Two-Level Weighted Collaborative Fuzzy Clustering Based on Maximum Entropy for Multi-View Data

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Tsinghua Science and Technology 2021年第2期26卷 185-198页

作者： Jie Hu yi pan Tianrui Li Yan Yang School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China Department of Computer Science Georgia State UniversityAtlantaGA 30302-3994USA

In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical *** the significant advances in multi-view clustering,two challenges still need to be addressed,i.e.,how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for *** this study,we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering(TW-Co-MFC)approach to address the aforementioned *** TW-Co-MFC,a two-level weighting strategy is devised to measure the importance of views and features,and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering *** an iterative optimization objective function based on the maximum entropy principle is designed for multi-view *** on real-world datasets show the effectiveness of the proposed approach.

关键词： multi-view clustering fuzzy clustering collaborative weighting maximum entropy

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Analysis of an improved acquisition method for high-dynamic BOC signal

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Journal of Systems Engineering and Electronics 2016年第6期27卷 1158-1167页

作者： yi pan Tianqi Zhang Gang Zhang Zhongtao Luo Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

To reach a compromise between short acquisition time and excellent detection probability for the high-dynamic binary offset carrier (BOC) signal, an overall algorithm based on discrete polynomial-phase transform (DPT) and partial matching filter (PMF)-fast Fourier transform (FFT) algorithm is given. The high order items of received signals are removed by the method of DPT, and the PMF-FFT algorithm is redesigned for the BOC signal. The simulation experiments and theoretical analyses verify that the improved algorithm has attained improvement in acquisition performance. © 2016 Beijing Institute of Aerospace Information.

关键词： Bins Mergers and acquisitions Titration

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