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Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu xiujuan lei Mei Ma 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

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG xiujuan lei Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

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

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： Microbe-disease association Heterogeneous network Global graph feature learning GraRep

基于北斗导航的出租车计价器在线检定方法研究

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品牌与标准化 2024年第2期 191-193页

作者：王尚德雷秀娟安娜张掖市计量检测中心甘肃张掖734000

本文深入探讨了出租车计价器在不需要拆机和上检测台的情况下,如何利用北斗精准导航技术进行在线检定。首先剖析了传统出租车计价器检定方法的局限性,然后详细介绍了北斗导航系统的功能特性与应用优势,接着提出了一种基于北斗导航的出... 详细信息

本文深入探讨了出租车计价器在不需要拆机和上检测台的情况下,如何利用北斗精准导航技术进行在线检定。首先剖析了传统出租车计价器检定方法的局限性,然后详细介绍了北斗导航系统的功能特性与应用优势,接着提出了一种基于北斗导航的出租车计价器在线检定方案,并对该方案的实施步骤进行了具体阐释,最后讨论了这一方案的潜在优势与局限,并预测了其在未来可能的应用场景。

关键词：北斗导航出租车计价器在线检定

CircR2Disease v2.0:An Updated Web Server for Experimentally Validated circRNA–disease Associations and Its Application

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 435-445页

作者： Chunyan Fan xiujuan lei Jiaojiao Tie Yuchen Zhang Fang-Xiang Wu Yi Pan School of Computer Science Shaanxi Normal UniversityXi’an 710119China Division of Biomedical Engineering University of SaskatchewanSaskatoonSK S7N 5A9Canada Department of Computer Science Georgia State UniversityAtlantaGA 30302USA

With accumulating dysregulated circular RNAs(circRNAs)in pathological processes,the regulatory functions of circRNAs,especially circRNAs as microRNA(miRNA)sponges and their interactions with RNA-binding proteins(RBPs),have been widely ***,the collected information on experimentally validated circRNA-disease associations is only ***,an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is ***,we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel ***2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous *** version includes 4201 entries between 3077 circRNAs and 312 disease ***,the information of circRNA-miRNA,circRNA-miRNA-target,and circRNA-RBP interactions has been manually collected for various ***,the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature *** descriptions such as samples and journals have also been integrated into the updated ***,CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in ***,we propose a computational method named circDis based on the graph convolutional network(GCN)and gradient boosting decision tree(GBDT)to illustrate the applications of the CircR2Disease v2.0 ***2Disease v2.0 is available at http://***/CircR2Disease_v2.0 and https://***/bioinforlab/CircR2Disease-v2.0.

关键词： circRNA circRNA-disease association Graph convolutional network Gradient boosting decision tree Machine learning

Essential Protein Prediction Based on Shuffled Frog-Leaping Algorithm

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Chinese Journal of Electronics 2021年第4期30卷 704-711页

作者： YANG Xiaoqin lei xiujuan ZHAO Jie School of Computer Science Shaanxi Normal University

Essential proteins are integral parts of living organisms. The prediction of essential proteins facilitates to discover disease genes and drug targets. The prediction precision and robustness of most of existing identification methods are not satisfactory. In this paper,we propose a novel essential proteins prediction method(EPSFLA), which applies Shuffled frog-leaping algorithm(SFLA), and integrates several biological information with network topological structure to identify essential ***, the topological property and several biological properties(function annotation, subcellular localization,protein complex, and orthology) are integrated and utilized to weight protein-protein interaction *** the position of a frog is encoded and denotes a candidate essential protein set. The frog population continuously evolve by means of local exploration and global exploration until termination criteria for algorithm are satisfied. Finally, those proteins contained in the best frog are regarded as predicted essential proteins. The experimental results show that EPSFLA outperforms some well-known prediction methods in terms of various *** proposed method aims to provide a new perspective for essential protein prediction.

关键词： Computational biology Essential protein Protein-protein interaction(PPI) network Shuffled frog leap algorithm

Logistic regression algorithm to identify candidate disease genes based on reliable protein-protein interaction network

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Science China(Information Sciences) 2021年第7期64卷 230-232页

作者： xiujuan lei Wenxiang ZHANG School of Computer Science Shaanxi Normal University

Dear editor,Genetic diseases have seriously threatened human health;however, identifying candidate disease genes is expected to contribute to understanding complex genetic diseases. Several machine learning algorithms... 详细信息

关键词： Disease genes Logistic regression algorithm Multiple biological data Topological similarity of heterogeneous network Reconstructed PPI network

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