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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

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

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

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： Information Systems and Communication Service

Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm

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Science China(Information Sciences) 2016年第7期59卷 30-43页

作者： xiujuan lei Yulian DING Fang-Xiang WU School of Computer Science Shaanxi Normal University School of Mathematical Sciences Nankai University Division of Biomedical Engineering University of Saskatchewan

Detecting protein complexes is crucial to understand principles of cellular organization. Plenty evidences have indicated that sub-graphs with high density in protein-protein interaction(PPI) network, especially dynamic PPI network(DPIN), usually correspond to protein complexes. As a well-known density-based clustering algorithm, Density-Based Spatial Clustering of Applications with Noise(DBSCAN) has been used in many areas due to its simplicity and the ability to detect clusters of different sizes and shapes. However, one of its limitations is that the performance of DBSCAN depends on two specified parameters ε and Min Pts, where εrepresents the maximum radius of a neighborhood from an observing point while Min Pts means the minimum number of data points contained in such a neighborhood. In this article, we develop a new method named as P-DBSCAN to detect protein complexes in DPIN by using Pigeon-Inspired Optimization(PIO) Algorithm to optimize the parameters ε and Min Pts in DBSCAN. The experiments on DIP and MIPS datasets show that P-DBSCAN outperforms the state-of-the-art methods for protein complex detection in terms of several criteria such as precision, recall and f-measure.

关键词： dynamic protein-protein interaction network(DPIN) pigeon-inspired optimization(PIO) protein complex density based clustering gene expression

Prediction of miRNA-circRNA Associations Based on k-NN Multi-Label with Random Walk Restart on a Heterogeneous Network

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Big Data Mining and Analytics 2019年第4期2卷 261-272页

作者： Zengqiang Fang xiujuan lei College of Computer Science Shaanxi Normal University

Circular RNAs(circRNAs) play important roles in various biological processes, as essential non-coding RNAs that have effects on transcriptional and posttranscriptional gene expression regulation. Recently, many studies have shown that circRNAs can be regarded as micro RNA(miRNA) sponges, which are known to be associated with certain diseases. Therefore efficient computation methods are needed to explore miRNAcircRNA interactions, but only very few computational methods for predicting the associations between miRNAs and circRNAs exist. In this study, we adopt an improved random walk computational method, named KRWRMC, to express complicated associations between miRNAs and circRNAs. Our major contributions can be summed up in two points. First, in the conventional Random Walk Restart Heterogeneous(RWRH) algorithm, the computational method simply converts the circRNA/miRNA similarity network into the transition probability matrix;in contrast,we take the influence of the neighbor of the node in the network into account, which can suggest or stress some potential associations. Second, our proposed KRWRMC is the first computational model to calculate large numbers of miRNA-circRNA associations, which can be regarded as biomarkers to diagnose certain diseases and can thus help us to better understand complicated diseases. The reliability of KRWRMC has been verified by Leave One Out Cross Validation(LOOCV) and 10-fold cross validation, the results of which indicate that this method achieves excellent performance in predicting potential miRNA-circRNA associations.

关键词： miRNA-circRNA associations heterogeneous network multi-label random walk restart

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CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization

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Big Data Mining and Analytics 2020年第4期3卷 280-291页

作者： Yuchen Zhang xiujuan lei Zengqiang Fang Yi Pan the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the Department of Computer Science Georgia State UniversityAtlantaGA 30302USA

Circular RNA(circRNA)is a novel non-coding endogenous *** has shown that circRNAs are related to many biological processes and play essential roles in different biological *** increasing numbers of circRNAs are discovered using high-throughput sequencing technologies,these techniques are still time-consuming and *** this study,we propose a computational method to predict circRNA-disesae associations which is based on metapath2 vec++and matrix factorization with integrated multiple data(called PCD MVMF).To construct more reliable networks,various aspects are ***,circRNA annotation,sequence,and functional similarity networks are established,and disease-related genes and semantics are adopted to construct disease functional and semantic similarity ***,metapath2 vec++is applied on an integrated heterogeneous network to learn the embedded features and initial prediction ***,we use matrix factorization,take similarity as a constraint,and optimize it to obtain the final prediction ***-one-out cross-validation,five-fold cross-validation,and f-measure are adopted to evaluate the performance of PCD *** evaluation metrics verify that PCD MVMF has better prediction performance than other *** further illustrate the performance of PCD MVMF,case studies of common diseases are ***,PCD MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.

关键词： circular RNAs(circRNAs) circRNA-disease associations matepath2vec++ matrix factorization

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