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Predicting Drug-Target Interaction Networks by Fusing Drug and Target Kernel Measures

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Predicting Drug-Target Interaction Networks by Fusing Drug a...

中国化学会第28届学术年会

作者： Dong-Sheng Cao1 Jian-Hua Huang1 Jun Yan1 Nai-Ping Dong1 Qian-Nan Hu4 Shao Liu3 Qing-Song Xu2 yi-zeng liang*1 1research center of modernization of traditional chinese medicines Central South University Changsha 410083 P.R.China 2School of Mathematical Sciences and Computing Technology Central South University Changsha 410083 P.R.China 3Xiangya Hospital Central South University Changsha 410008 P.R.China 4Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University) Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 P.R.China research center of modernization of traditional chinese medicines Central South UniversityChangsha 410083 P.R. China Key Laboratory of Combinatorial Biosynthesis and Drug Discovery(Wuhan University)Ministry of Education and Wuhan University School of Pharmaceutical SciencesWuhan 430071 P.R.China Xiangya Hospital Central South University Changsha 410008 P. R. China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083 P.R.China

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug *** is both consuming and costly to determine drug-target interactions by experiments ***,there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner.A key issue for predicting drug-target interactions is how to represent the interaction relationship between drugs and target proteins by using a certain numeric *** the work,we aim at addressing such an issue by using some basic properties of kernel *** means of different kernel representations,drug similarity can be represented by constructing a drug kernel matrix,and protein similarity a protein kernel *** interactions between drugs and target proteins are represented by some certain combination of drug kernel and target *** can be considered as a new interaction kernel representing complex interaction relationships between drugs and targets,strictly under the framework of basic kernel ***,we redesigned the question as a binary classification one by support vector machines （SVMs） with an interaction kernel as an *** classes of drug-target interaction networks in humans involving enzymes,ion channels,G-protein-coupled receptors （GPCRs） and nuclear receptors,are independently used for establishing predictive models using SVM as their operation engine,with each to predict the interactions between drugs and one of the four protein *** conclusion,the results demonstrate the ability of our proposed method to predict the drug-target interactions.

关键词： drug-target interaction data fusion molecular fingerprint networks genomic drug discovery kernel methods.

Genome-scale screening of drug-target associations relevant to Ki binding affinity using a chemogenomics approach

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Genome-scale screening of drug-target associations relevant ...

中国化学会第28届学术年会

作者： Dong-Sheng Cao1 Zhe Deng2 Liu-Xia Zhang3 Guang-Hua Zhou3 Jian-Hua Huang1 Qian-Nan Hu2 Qing-Song Xu4 yi-zeng liang*1 1research center of modernization of traditional chinese medicines Central South University Changsha 410083 P.R.China 2Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University) Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 P.R.China 3The 163th hospital of The chinese People’s Liberation Army Changsha 410083 P.R.China 4School of Mathematical Sciences and Computing Technology Central South University Changsha 410083 P.R.China research center of modernization of traditional chinese medicines Central South UniversityChangsha 410083 P.R. China Key Laboratory of Combinatorial Biosynthesis and Drug Discovery(Wuhan University)Ministry of Education and Wuhan University School of Pharmaceutical SciencesWuhan 430071 P.R.China The 163th hospital of The Chinese People''s Liberation ArmyChangsha 410083 P.R.China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083 P. R. China

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery,especially for those with strong binding *** is both consuming and costly to determine drug-target interactions by experiments *** rapidly increasing amount of publicly available knowledge in biology and chemistry enables researchers to revisit drug-target interaction problems by the systematic integration and analysis of heterogeneous novel ***,we develop a chemogenomics framework which can accurately identify stronger drug-target interactions using only an unbiased set of general integrated *** forest （RF） is then employed to construct a predictive model which can accurately classify drug-target pairs with different thresholds of binding affinity in these ***,we find that the predictive features identified by the RF classifier reveal biologically relevant elements underlying in the mechanism of *** our RF is trained on experimental data,we also predict novel drug-target associations,and show that the putative drug-target pairs can be validated from public biological *** also reconstruct a drug-target interaction network with high predictive confidence drug-target *** a network provides further insights into drug-target ***,we developed a web-based server,called PreDPI-Ki,for predicting drug-target interactions with strong Ki binding affinity for drug *** addition to providing a high confidence list of drug-target associations for subsequent experimental investigation,these results contribute to our understanding of drug-target ***-Ki is freely available via:http://***/onlineplatform/dpi1

关键词： genomic drug discovery network analysis random forest chemogenomics drug-target interaction.

A Novel Kernel Fisher Discriminant Analysis:Constructing Informative Kernel by Decision Tree Ensemble for Metabolomics Data Analysis

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A Novel Kernel Fisher Discriminant Analysis:Constructing Inf...

中国化学会第28届学术年会

作者： Dong-Sheng Cao1 Mao-Mao zeng2 Jun Yan1 Jian-Hua Huang1 Qian-Nan Hu4 Qing-Song Xu3 yi-zeng liang*1 1research center of modernization of traditional chinese medicines Central South University Changsha 410083 P.R.China 2State Key Laboratory of Food Science and Technology School of Food Science and Technology Jiangnan Univesity Wuxi 214122 P.R.China 3School of Mathematical Sciences and Computing Technology Central South University Changsha 410083 P.R.China 4Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University) Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 P.R.China research center of modernization of traditional chinese medicines Central South UniversityChangsha 410083 P. R. China State Key Laboratory of Food Science and Technology School of Food Science and Technology Jiangnan Univesity Wuxi 214122 P.R.China Research center of modernization of traditional Chinese medicines Central South UniversityChangsha 410083 P.R. China Key Laboratory of Combinatorial Biosynthesis and Drug Discovery(Wuhan University)Ministry of Education and Wuhan University School of Pharmaceutical SciencesWuhan 430071 P.R.China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083 P.R.China

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex,which brings an enormous amount of challenges to existing statistical *** there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under *** the work,we developed a novel kernel Fisher discriminant analysis （KFDA） algorithm by constructing an informative kernel based on decision tree *** constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement ***,informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building ***,KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some ***,two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.

关键词： metabolomics kernel methods Fisher discriminant analysis (FDA) biomarker discovery decision tree classification and regression tree (CART)

Aspects of recent developments in analytical chemometrics

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Science China Chemistry 2006年第3期49卷 193-203页

作者： liang yizeng1,2, WU Hailong1, SHEN Guoli1, JIANG Jianhui1, liang Sheng2 & YU Ruqin1 1. State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China 2. research center of modernization of traditional chinese medicines, Central South University, Changsha 410081, China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical Engineering Hunan University Changsha China research center of modernization of traditional chinese medicines Central South University Changsha China

Some aspects of recent developments in analytical chemometrics are discussed, in par- ticular the developments viewed from the angle of the research efforts undertaken in authors’ labora- tories. The topics concerned include resolution of high-order chemical data, morphological theory and methodology for chemical signal processing, multivariate calibration and chemical pattern recognition for solving complex chemical problems, and resolution of two-way chemical data from hyphenated chromatographic instruments.

关键词： chemometrics, high-order chemical data, pattern recognition, multivariate calibration and resolution.

维普期刊数据库

Analysis of volatile oil in Rhizoma Ligustici Chuanxiong-Radix Paeoniae Rubra by gas chromatography-mass spectrometry and chemometric resolution

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Acta Pharmacologica Sinica 2006年第4期27卷 491-498页

作者： Xiao-ru LI yi-zeng liang~2 Fang-qiu GUO College of Chemistry and Chemical Engineering,research center of modernization of traditional chinese medicines,Central South University,Changsha 410083.China CollegeofChemistryandChemicalEngineering ResearchCenterofModernizationofTraditionalChineseMedicinesCentralSouthUniversityCha

Aim:To analyze the volatile chemical components of the herbal pair RhizomaLigustici Chuanxiong-Radix Paeoniae Rubra(RLC-RPR)and compare them withthose of each of the herbs ***:Gas chromatography-mass spectrom-etry(GC-MS),a chemometric resolution technique using the heuristic evolvinglatent projections(HELP)method,and the overall volume integration methodwere ***:In total,52,38,and 61 volatile chemical components in RLC,RPR,and RLC-RPR essential oils were determined,respectively,accounting for95.14%,95.19%,and 89.68% of the total contents of essential oil of RLC,RPR,andRLC-RPR,*** main volatile chemical components werebutyldienephthalide(20.65%)and ligustilide(50.15%)for RLC;and n-hexadecanoicacid(20.18%),[Z,Z]9,12-octadecadienoic acid(30.11%),2-hydroxy-benzaldehyde(17.08%)for RPR,and butyldienephthalide(14.80%),and ligustilide(38.91%)*** main volatile chemical components of RLC-RPR were almost thesame as those of RLC,but the relative amounts were ***:Thenumber of volatile chemical components in RLC-RPR was almost equal to the sumof the number in the 2 constituent herbs,but the relative amounts were ***,an acid-base reaction takes place during the process of decoctingthe *** data gathered in this study may be helpful for understanding thesynergistic nature of this herb pair in traditional chinese medicine.

关键词： herbal pair Rhizoma Ligustici Chuanxiong-Radix Paeoniae Rubra essential oil gas chromatography-mass spectrometry chemometric resolution method

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高效液相色谱法测定食物中的类胡萝卜素

色谱 2005年第1期23卷 57-62页

作者：卢红梅梁逸曾中南大学化学化工学院中药现代化研究中心湖南长沙410083

综述了用于食物中的类胡萝卜素、类胡萝卜素酯测定的高效液相色谱方法,介绍了样品提取处理方法、色谱分离条件及检测方法。引用文献85篇。

关键词：高效液相色谱法类胡萝卜素食物分析综述

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Gain deeper insights into traditional chinese medicines using multidimensional chromatography combined with chemometric approaches

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chinese Herbal medicines 2024年第1期16卷 27-41页

作者： Xinyue Yang Pingping zeng Jin Wen Chuanlin Wang liangyuan Yao Min He Department of Pharmaceutical Engineering School of Chemical EngineeringXiangtan UniversityXiangtan 411105China Hunan Qianjin Xiangjiang Pharmaceutical Joint Stock Co. Ltd.Zhuzhou 412000China

traditional chinese medicines(TCMs)possess a rich historical background,unique theoretical framework,remarkable therapeutic efficacy,and abundant ***,the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown *** gain deeper insights into the phytochemicals and ensure the quality control of TCMs,there is an urgent need to enhance analytical ***,two-dimensional(2D)chromatography,which incorporates two independent separation mechanisms,demonstrates superior separation capabilities compared to the traditional one-dimensional(1D)separation system when analyzing TCMs *** the past decade,new techniques have been continuously developed to gain actionable insights from complex *** review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs,encompassing 2D-gas chromatography(GC),2D-liquid chromatography(LC),as well as emerging three-dimensional(3D)-GC,3D-LC,and their associated data-processing *** studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical ***,the increased separation capability has resulted in higher-order data sets and greater demands for data-processing *** that multidimensional chromatography is still a relatively nascent research field,further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.

关键词： chemometrics multidimensional separation off-line two-dimensional liquid chromatography on-line two-dimensional liquid chromatography quality control traditional chinese medicines two-dimensional gas chromatography

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Counter current chromatography numerical simulation

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Counter current chromatography numerical simulation

第十届全国计算(机)化学学术会议

作者： Zhaohui Yang,Bing Wang,yizeng liang (College of Chemistry and Chemical Engineering,research center of modernization of chinese medicines, Central South University,Changsha,410083,People’s Republic of China,)

For measuring a certain solute’s concentration distribution in counter current chromatography tube,a numerical model was suggested for calculation of the retention volume (V) and simulation of ***-Stage ratio(RSR) was introduced to describe retention time;it has positive proportion to the retention time(RSR =(F/V)t,where F is the flow rate;V is the balance mobile phase volume;t is time.).RSR(RSR =[(1-S+ kS)/(1 - S)](1 -1/n), where S is the retention ratio of stationary phase,n is stages number.) is a constant for a specified k value for a large number of stages(k value is defined as the proportion of concentration in the stationary phase and mobile phase).For a certain material,the concentration distribution is a Negative Binomial(x= Cpqx,where x is the concentration for a solute which distribution ratio is k,x is the initial concentration,p=(1-S)/(1-S+kS), q = kS/(1-S+kS)).For some solvent system,the consumed time for the balance of the solvents is very long and the consumption of solvents is costly,solvents reuse became very important,especially for the balanced ***-feed is essential for such a time and money consumed *** numerical model can predict the optimal feed *** influence to the process for the amount of a feeding solute is discussed *** efficiency(E),which was defined as E = n/n(n is stages,which can be measured by the equation n=1/2πqX,where X is the dimensionless concentration.),was introduced to determine the effect to the spectrum by different k values of different components.

关键词： numerical model retention volume row-stage ratio negative binomial multi-feed stage efficiency

A NOVEL 3D MOLECULAR SIMILARITY ALGORITHM BASED ON ENERGY SPECTRUM

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A NOVEL 3D MOLECULAR SIMILARITY ALGORITHM BASED ON ENERGY SP...

40th IUPAC Congress

作者： Wei Liu Jun Xu Shuo Chen yizeng liang * research center of modernization of traditional chinese Herb Medicine College of Chemistry and Chemical Engineering Central South University Changsha 410083 China.