检索结果-内蒙古大学图书馆

BPS2025 - Kinetic modulation of α-synuclein fibrillation and toxicity by 4-phenylbutyric acid

Biophysical Journal 2025年第3期124卷 238a-238a页

作者： Tuhina Banerjee Kristos Baffour Neelima Koti David Johnson Santimukul Santra Department of Chemistry and Biochemistry Missouri State University Springfield MO USA Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS USA

来源：评论

学校读者我要写书评

暂无评论

BPS2025 - Membrane fusion interactions of enveloped viruses using lipid-coated iron oxide nanosensors

引用

Biophysical Journal 2025年第3期124卷 498a-498a页

作者： Santimukul Santra David Johnson Tuhina Banerjee Department of Chemistry and Biochemistry Missouri State University Springfield MO USA Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS USA

来源：评论

学校读者我要写书评

暂无评论

Cover Picture: Enantiospecific Synthesis and Cytotoxicity Evaluation of Oximidine II Analogues (ChemMedChem 15/2016)

引用

ChemMedChem 2016年第15期11卷 1578-1578页

作者： Dr. Christopher M. Schneider Dr. Wei Li Dr. Kriangsak Khownium Dr. Gerald H. Lushington Prof. Dr. Gunda I. Georg Department of Medicinal Chemistry University of Kansas 1251 Wescoe Hall Drive Lawrence KS 66045 USA Department of Medicinal Chemistry Institute for Therapeutics Discovery and Development University of Minnesota 717 Delaware Street SE Minneapolis MN 55414 USA Molecular Graphics & Modeling Laboratory University of Kansas 1251 Wescoe Hall Drive Lawrence KS 66045 USA

来源：评论

学校读者我要写书评

暂无评论

KUChemBio: A repository of computational chemical biology data sets

KUChemBio: A repository of computational chemical biology da...

引用

IEEE International Conference on Big Data

作者： Aaron Smalter Hall Jun Huan Molecular Graphics and Modeling Laboratory University of Kansas Lawrence Kansas United States Dept. of Electrical Engineering and Computer Science University of Kansas Lawrence Kansas United States

ISBN: (纸本)9781479912940

Data set curation in cheminformatics is largely ignored, and many publications do not provide the specific chemical structures used in their experiments. Access to chemical structures is vital for experiment reproducibility and comparison of competing methods. To address this limitation, the KU Chemical Biology Database (KUChemBio) has established a collection of 69 data sets for computational chemical biology experiments. Data sets fall into several categories including ADME, toxicity, binding affinity, solubility, melting points, and others. Chemical structures in SDF or Smiles format are provided along with binary or real valued activity labels. Data sets have been consolidated from other online repositories and content from recent publications has been added as well. KUChemBio is located at http://***/kuchembio.

关键词： Chemicals Compounds Absorption Drugs Biology Inhibitors Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Exploratory analysis of the BioAssay Network with implications to therapeutic discovery

Exploratory analysis of the BioAssay Network with implicatio...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jintao Zhang Gerald H. Lushington Jun Huan Center of Bioinformatics University of Kansas Lawrence KS USA Molecular Graphics & Modeling Laboratory University of Kansas Lawrence KS USA Department of Electrical Engineering and Computer Sciences University of Kansas Lawrence KS USA

Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the molecular Libraries Initiative (MLI) in 2004 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. In this paper we used the concepts of network biology to integrate MLI data with other biological databases such as DrugBank and UniHI, and evaluated the potential of MLI target proteins being new drug targets. Our analysis provided some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery.

关键词： Drugs Proteins Humans Compounds Bioinformatics Databases

来源：评论

学校读者我要写书评

暂无评论

G-hash: Towards fast kernel-based similarity search in large graph databases 09

G-hash: Towards fast kernel-based similarity search in large...

引用

12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT'09

作者： Wang, Xiaohong Smalter, Aaron Huan, Jun Lushington, Gerald H. Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS United States Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS United States

ISBN: (纸本)9781605584225

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others. Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database. Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the inde

关键词： Query processing

来源：评论

学校读者我要写书评

暂无评论

Feature Selection in the Tensor Product Feature Space

Feature Selection in the Tensor Product Feature Space

引用

IEEE International Conference on Data Mining (ICDM)

作者： Aaron Smalter Jun Huan Gerald Lushington Department of Electrical Engineering and Computer Science University of Kansas USA Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS USA

Classifying objects that are sampled jointly from two or more domains has many applications. The tensor product feature space is useful for modeling interactions between feature sets in different domains but feature selection in the tensor product feature space is challenging. Conventional feature selection methods ignore the structure of the feature space and may not provide the optimal results. In this paper we propose methods for selecting features in the original feature spaces of different domains. We obtained sparsity through two approaches, one using integer quadratic programming and another using L1-norm regularization. Experimental studies on biological data sets validate our approach.

关键词： Tensile stress Kernel Proteins Predictive models Application software Data mining Computer graphics Laboratories Industry applications Stacking

来源：评论

学校读者我要写书评

暂无评论

Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

Application of Kernel Functions for Accurate Similarity Sear...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaohong Wang Jun Huan Aaron Smalter Gerald H. Lushington School of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS USA

Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.

关键词： Kernel Databases Indexing Genomics Bioinformatics Drugs Probes Predictive models Chemical compounds Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Chemical compound classification with automatically mined structure patterns

Chemical compound classification with automatically mined st...

引用

6th Asia-Pacific Bioinformatics Conference, APBC 2008

作者： Smalter, A.M. Huan, J. Lushington, G.H. Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS 66045 United States Molecular Graphics and Modeling Laboratory University of Kansas Lawrence KS 66045 United States

ISBN: (纸本)9781848161085

In this paper we propose new methods of chemical structure classification based on the integration of graph database mining from data mining and graph kernel functions from machine learning. In our method, we first identify a set of general graph patterns in chemical structure data. These patterns are then used to augment a graph kernel function that calculates the pairwise similarity between molecules. The obtained similarity matrix is used as input to classify chemical compounds via a kernel machines such as the support vector machine (SVM). Our results indicate that the use of a pattern-based approach to graph similarity yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art approaches. In addition, the identification of highly discriminative patterns for activity classification provides evidence that our methods can make generalizations about a compound's function given its chemical structure. While we evaluated our methods on molecular structures, these methods are designed to operate on general graph data and hence could easily be applied to other domains in bioinformatics.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

GPD: A graph pattern diffusion kernel for accurate graph classification with applications in cheminformatics 8

GPD: A graph pattern diffusion kernel for accurate graph cla...

引用

8th International Workshop on Data Mining in Bioinformatics, BIOKDD 2008 - Held in conjunction with 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2008

作者： Smalter, Aaron Huan, Jun Yi, Jia Lushington, Gerald H. Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66047-7621 United States Molecular Graphics and Modeling Laboratory University of Kansas LawrenceKS United States

ISBN: (纸本)3642125182

Graph data mining is an active research area. Graphs are general modeling tools to organize information from het-erogenous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. In this paper, we demonstrate a novel technique called graph pattern diffusion kernel (GPD). Our idea is to leverage existing frequent pattern discovery methods and to explore the application of kernel classifier (e.g. support vector machine) in building highly accurate graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the graph database and use a process we call "pattern diffusion" to label nodes in the graphs. Finally we designed a novel graph alignment algorithm to compute the inner product of two graphs. We have tested our algorithm using a number of chemical structure data. The experimental results demonstrate that our method is significantly better than competing methods such as those kernel functions based on paths, cycles, and subgraphs. © 2008 ACM

关键词： Graph Databases

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：