2006 IEEE International Conference on Granular Computing最新文献_第10页

Identifying the most significant genes from gene expression profiles for sample classification 从基因表达谱中识别最重要的基因用于样本分类

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635887

H. Al-Mubaid, Noushin Ghaffari

{"title":"Identifying the most significant genes from gene expression profiles for sample classification","authors":"H. Al-Mubaid, Noushin Ghaffari","doi":"10.1109/GRC.2006.1635887","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635887","url":null,"abstract":"The gene expression data generated by the Microarray technology for thousands of genes simultaneously provide huge amounts of biomedical data in forms of gene expression profiles. This generated gene data include complex variations of expression levels of thousands of gene in the classes of samples. The gene level variations allow for classifying and clustering the samples based on only a small subset of genes. In this work, we want to identify the most significant genes that demonstrate the highest capabilities of discrimination between the classes of samples. We present a new gene selection technique for extracting the most significant genes from the huge gene/feature space in a given gene expression dataset. Our method is based on computing the discriminating capability of each gene, and classifying the data according to only those most significant genes that have highest discriminating capabilities. We also adapted from text categorization and information retrieval five feature selection techniques into the gene selection task to compare with our method. We evaluated the method using four well-known gene expression datasets. The experimental results showed that our method produces impressive and competitive results in terms of classification performance with few selected genes compared with the existing techniques.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127044245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A cutting plane algorithm for multiclass kernel discriminations 多类核判别的切割平面算法

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635787

Tien-Fang Kuo, Yasutoshi Yajima

引用次数: 0

Flooding isolated region reassignment 洪水孤立区域重新分配

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635815

S. Makki, David A. Heitbrink, Xiaohua Jia

引用次数: 1

Dependence space of concept lattices based on rough set 基于粗糙集的概念格依赖空间

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635783

Jianmin Ma, Wenxiu Zhang, Xia Wang

引用次数: 14

Cysteine separations profiles on protein secondary structure infer disulfide connectivity 半胱氨酸在蛋白质二级结构上的分离图谱推断出二硫连通性

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635889

Guantao Chen, Hai Deng, Yufeng Gui, Yi Pan, Xue Wang

{"title":"Cysteine separations profiles on protein secondary structure infer disulfide connectivity","authors":"Guantao Chen, Hai Deng, Yufeng Gui, Yi Pan, Xue Wang","doi":"10.1109/GRC.2006.1635889","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635889","url":null,"abstract":"Disulfide connectivity prediction from one chain of protein helps determine protein tertiary structure. The more accuracy of prediction it reaches the more precise three dimensional structures we can obtain through computational methods. Previous methods only use local sequence or secondary structure information or global sequence information or combination of the above descriptors to predict the disulfide bond pattern. Instead of using those descriptors, we take an alternative descriptor of global secondary structure to make prediction, and the highest performance among all pattern-wise methods is obtained. Cysteine separation profiles on protein secondary structure have been used to predict the disulfide connectivity of proteins. The cysteine separation profiles on secondary structure(CSPSS) represent a vector encoded from the sepeartions between any two consecutive cysteine-corresponding positions in a predicted protein secondary structure sequence. Through comparisons of their CSPSS, the disulfide connectivity of a test protein is inferred from a template set. In 4-fold of SP39, any two proteins from different groups share less than 30% sequence identity. The result shows a prediction accuracy (54%), which proves again a disulfide bond pattern is highly related to protein secondary structure.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"140 18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124336258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

XHMG: Content-Based Web HyperMedia Modeling and Retrieval System 基于内容的Web超媒体建模与检索系统

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635895

I. Radev

引用次数: 0

A granular computing view on function approximation 函数近似的颗粒计算观点

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635789

Xiao-Jun Zeng, J. Keane

引用次数: 1

The entropy for rough fuzzy sets 粗糙模糊集的熵

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635802

Z. Chengyi, Wei Bencheng, Cheng Guohui, Fu Haiyan

引用次数: 2

Agent-based peer-to-peer layered architecture for data transfer in wireless sensor networks 无线传感器网络中基于agent的点对点分层数据传输体系结构

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635847

E. Shakshuki, S. Hussain, A. W. Matin, A. R. Matin

{"title":"Agent-based peer-to-peer layered architecture for data transfer in wireless sensor networks","authors":"E. Shakshuki, S. Hussain, A. W. Matin, A. R. Matin","doi":"10.1109/GRC.2006.1635847","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635847","url":null,"abstract":"Recently, there has been a growing interest in the potential use of Wireless Sensor Networks (WSNs) in many applications such as smart environments, disaster management, combat field reconnaissance, and security surveillance. Therefore, to realize their potential, there is a need of an architecture that facilities the deployment of a network that is optimized in terms of energy, query and network configuration. This paper focuses on developing agent-based peer-to-peer layered system architecture for data transfer in WSNs. The architecture has three layers: application, database and network. At each layer, agents interact as peers; however, agents at base- station are computation intensive and agents at sensor nodes require very limited energy and computing resources. The application layer is the highest layer where peers exchange data requests and results. The database layer is the middle layer where peers exchange query execution plans and the query results. The network layer is the lowest layer where peers exchange the routing information and sensor data. The proposed system is implemented in Java and mica2 motes.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128274151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Collision detection for deforming linear objects using particle swarm optimization 基于粒子群优化的线性变形物体碰撞检测

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635841

Wang Yi, Liu Wenhui, Wang Tianzhu, Han Dongfeng, Meng Yu

引用次数: 2