期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到9篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Integration of deep neural network modeling and LC-MS-based pseudo-targeted metabolomics to discriminate easily confused ginseng species 认领 引用 被引量：2
1
作者 Meiting Jiang Yuyang Sha +8 位作者 Yadan Zou Xiaoyan Xu Mengxiang Ding Xu Lian Hongda Wang Qilong Wang Kefeng Li De-an Guo Wenzhi Yang 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第1期126-137,共12页
Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments invo... Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector machine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng. 展开更多
关键词 Liquid chromatography-mass spectrometry Pseudo-targeted metabolomics Deep neural network Species differentiation Ginseng
暂未订购 下载PDF
Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2 library: Sishen formula as a case 认领 引用
2
作者 Lili Hong Wei Wang +9 位作者 Shiyu Wang Wandi Hu Yuyang Sha Xiaoyan Xu Xiaoying Wang Kefeng Li Hongda Wang Xiumei Gao De-an Guo Wenzhi Yang 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2024年第10期1484-1495,共12页
Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,... Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,and the results highly rely on the personal knowledge and experience.The goal of this work was to establish a software-aided approach,by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS)and in-house high-definition MS2 library,to enhance the identification of prototypes and metabolites of the compound formulae in vivo,taking Sishen formula(SSF)as a template.Seven different MS2 acquisition methods were compared,which demonstrated the potency of a hybrid scan approach(namely high-definition data-independent/data-dependent acquisition(HDDIDDA))in the identification precision,MS1 coverage,and MS2 spectra quality.The HDDIDDA data for 55 reference compounds,four component drugs,and SSF,together with the rat bio-samples(e.g.,plasma,urine,feces,liver,and kidney),were acquired.Based on the UNIFI™platform(Waters),the efficient data processing workflows were established by combining mass defect filtering(MDF)-induced classification,diagnostic product ions(DPIs),and neutral loss filtering(NLF)-dominated structural confirmation.The high-definition MS2 spectral libraries,dubbed in vitro-SSF and in vivo-SSF,were elaborated,enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples.Consequently,118 prototypes and 206 metabolites of SSF were identified,with the identification rate reaching 80.51%and 79.61%,respectively.The metabolic pathways mainly involved the oxidation,reduction,hydrolysis,sulfation,methylation,demethylation,acetylation,glucuronidation,and the combined reactions.Conclusively,the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner. 展开更多
关键词 Ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS) Hybrid scan High-definition MS2spectral library Sishen formula
暂未订购 下载PDF
Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome 认领 引用 被引量：2
3
作者 Junrong Li Xiaobing Zhai +6 位作者 Jixing Liu Chi Kin Lam Weiyu Meng Yuefei Wang Shu Li Yapeng Wang Kefeng Li 《Acupuncture and Herbal Medicine》 2024年第1期122-133,共12页
Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a cli... Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e−05)and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e−08)in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS. 展开更多
关键词 Causal factors Causal graph analysis Chronic fatigue syndrome Drug targets Mendelian randomization Qingjin Yiqi
暂未订购 下载PDF
Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction 认领 引用 被引量：1
4
作者 Shuo Liu Mengyun Chen +1 位作者 Xiaojun Yao Huanxiang Liu 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第6期1311-1320,共10页
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me... Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates. 展开更多
关键词 Deep learning Hierarchical molecular graph Molecular fingerprint Molecular property prediction Directed message-passing neural network
暂未订购 下载PDF
Achieving negative thermal expansion over an extended temperature range in rare-earth-modified Pb TiO3-based perovskites 认领 引用 被引量：1
5
作者 Zhao Pan Meng-Qi Ye +12 位作者 Yan Suo Feng-Yi Zhou Duo Wang Jin Liu Xu-Bin Ye Jie Zhang Mao-Cai Pi Wei-Hao Li Chao Chen Nian-Peng Lu Shogo Kawaguchi Yao Shen You-Wen Long 《Rare Metals》 SCIE EI CAS CSCD 2025年第9期6494-6502,共9页
Negative thermal expansion(NTE)is a notable physical property where a material’s volume decreases instead of increasing when heated.The identification of NTE materials is crucial for thermal expansion control enginee... Negative thermal expansion(NTE)is a notable physical property where a material’s volume decreases instead of increasing when heated.The identification of NTE materials is crucial for thermal expansion control engineering.Most NTE materials exhibit NTE only within a narrow temperature range,restricting their applications.Achieving NTE across a broad temperature range remains a significant challenge.This study developed a novel PbTiO3-based system,(1-x)PbTiO3–xBiLuO3,incorporating rare-earth elements,using a distinctive high-pressure and high-temperature synthesis technique.We achieved NTE across a broad temperature range by coupling lattice(c/a)with ferroelectric order parameters.The incorporation of BiLuO3resulted in distinctive ferroelectric characteristics,including increased tetragonality,spontaneous polarization,and NTE over a broad temperature range.NTE over an extended temperature range has been achieved in 0.95PbTiO3–0.05BiLuO3(■=−1.7×10–5K−1,300–840 K)and 0.90PbTiO3–0.10BiLuO3(■=−1.4×10–5K−1,300–860 K),compared to pristine PbTiO3(■=−1.99×10–5K−1,300–763 K).The improved tetragonalities and broader NTE temperature range result from the strong hybridization of Pb/Bi–O and Ti/Lu–O atoms,as demonstrated by combined experimental and theoretical analyses,including high-energy synchrotron X-ray diffraction,Raman spectroscopy,and density functional theory calculations.This study introduces a novel example of NTE over a broad temperature range,highlighting its potential as a high-performance thermal expansion compensator.Additionally,it presents an effective method for incorporating rare-earth elements to achieve NTE in PbTiO3-based perovskites across a wide temperature range. 展开更多
关键词 Negative thermal expansion High-pressure and high-temperature synthesis Density functional theory
暂未订购 下载PDF
HyPepTox-Fuse:An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors 认领 引用 被引量：2
6
作者 Duong Thanh Tran Nhat Truong Pham +2 位作者 Nguyen Doan Hieu Nguyen Leyi Wei Balachandran Manavalan 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第8期1873-1886,共14页
Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial fo... Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics.While traditional experimental approaches are time-consuming and expensive,computational methods have emerged as viable alternatives,including similarity-based and machine learning(ML)-/deep learning(DL)-based methods.However,existing methods often struggle with robustness and generalizability.To address these challenges,we propose HyPepTox-Fuse,a novel framework that fuses protein language model(PLM)-based embeddings with conventional descriptors.HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture.A robust feature ranking and selection pipeline further refines conventional descriptors,thus enhancing prediction performance.Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations,offering a scalable and reliable tool for peptide toxicity prediction.Moreover,we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse,highlighting its effectiveness in enhancing model performance.Furthermore,the HyPepTox-Fuse server is freely accessible at http://gffzza755ec5574b74ebesn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/HyPepTox-Fuse/and the source code is publicly available at http://gffzz188fe103f8f1460asn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/cbbl-skku-org/HyPepTox-Fuse/.The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets. 展开更多
关键词 Peptide toxicity Hybrid framework Multi-head attention Transformer Deep learning Machine learning Protein language model
暂未订购 下载PDF
Machine Learning Enables Comprehensive Prediction of the Relative Protein Abundance of Multiple Proteins on the Protein Corona 认领 引用 被引量：3
7
作者 Xiuhao Fu Chao Yang +9 位作者 Yunyun Su Chunling Liu Haoye Qiu Yanyan Yu Gaoxing Su Qingchen Zhang Leyi Wei Feifei Cui Quan Zou Zilong Zhang 《Research》 SCIE EI CSCD 2025年第2期785-800,共16页
Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important paramete... Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://gffzz21d9873f627c4b5fhn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/PC_ML. 展开更多
关键词 dichotomous predictionthenwe protein corona biomedicinerelative protein abundance rpa accounting multiple machine learning algorithms machine learning relative protein abundance predictive modeling interpretable analysis
Application of an improved ACO integrating BFS and Laplacian smoothing strategy in mobile robot path planning 认领 引用
8
作者 Shuai Wu Zibo Huang +1 位作者 Zijing Ye Qingxia Li 《International Journal of Intelligent Computing and Cybernetics》 EI 2025年第4期759-790,共32页
Purpose–In mobile robot path planning,algorithms such as PSO and GA are widely applied but have issues such as premature convergence and insufficient path smoothness.Although ant colony optimization(ACO)has advantage... Purpose–In mobile robot path planning,algorithms such as PSO and GA are widely applied but have issues such as premature convergence and insufficient path smoothness.Although ant colony optimization(ACO)has advantages in path diversity and global search capability,it faces limitations including poor initial guidance,slow convergence,time-consuming computation and excessive redundant turning points.This paper proposes an enhanced ACO integrating multiple improvement strategies to accelerate convergence,improve search efficiency,smooth trajectories and enhance the overall execution efficiency of robots.Design/methodology/approach–The method first uses BFS to pre-search a feasible path,which is smoothed and used to enhance pheromone concentration,improving the ants’initial search direction.A Sigmoid dynamic heuristic factor accelerates convergence,while a dynamic pheromone evaporation rate balances global exploration and local exploitation.The pheromone update equation has been improved to prevent the overuse of frequently selected edges,thereby avoiding premature convergence to local optima.Edge usage rate information further balances exploration and exploitation.Finally,Laplacian smoothing is applied to the path to remove discrete points and sharp turns,resulting in a natural and coherent trajectory.Findings–Simulations show that the improved ACO outperforms four existing algorithms in convergence speed,number of turning points and path smoothness,confirming its effectiveness in finding optimal and practical trajectories.Practical implications–This method holds broad future promise in the field of mobile robotics,enabling intelligent systems to achieve more efficient and safer autonomous navigation across diverse scenarios.By significantly enhancing task execution speed and resource utilization,it lays a solid foundation for the widespread adoption and sustained development of mobile robotics technology.Originality/value–This paper introduces a novel integration of BFS-based pre-search with pheromone enhancement,a Sigmoid dynamic heuristic factor,dynamic pheromone evaporation and improved pheromone updating based on edge usage rates,collectively addressing traditional ACO’s weaknesses.The application of Laplacian smoothing further refines path quality.These contributions significantly improve converge. 展开更多
关键词 Mobile robot ACO BFS Laplacian Path planning
Deep reinforcement learning for near-fieldwideband beamforming in STAR-RIS networks 认领 引用 被引量：1
9
作者 Ji WANG Jiayi SUN +3 位作者 Wei FANG Zhao CHEN Yue LIU Yuanwei LIU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2024年第12期1651-1663,共13页
A simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)assisted multiuser near-field wideband communication system is investigated,in which a robust deep reinforcement learning(DRL)ba... A simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)assisted multiuser near-field wideband communication system is investigated,in which a robust deep reinforcement learning(DRL)based algorithm is proposed to enhance the users’achievable rate by jointly optimizing the active beamforming at the base station(BS)and passive beamforming at the STAR-RIS.To mitigate the beam split issue,the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming.Considering the coupled nature of the STARRIS phase-shift model,the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control,and the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping.Additionally,to address the issue of biased estimation encountered by existing DRL algorithms,a softmax operator is introduced into the algorithm to mitigate this bias.Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation. 展开更多
关键词 Deep reinforcement learning Near-field beamforming Simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS) Wideband beam split
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈