2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)最新文献_第9页

Approximation for preemptive scheduling stochastic jobs on identical parallel machines 同一并行机上随机作业的抢占调度近似

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645076

Xiaoyong Tang, Kenli Li, Fan Wu

引用次数: 2

Optimal reservoir operation using a hybrid Simulated Annealing Algorithm-Genetic Algorithm 基于混合模拟退火算法-遗传算法的水库优化调度

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645168

Yong-yong Zhang, Qiang Huang, Fan Gao, Xiao-yi Sun

引用次数: 5

An improved genetic algorithm for wireless sensor networks localization 一种改进的无线传感器网络定位遗传算法

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645165

Gao Yang, Zhuang Yi, Ni Tianquan, Yi Keke, Xue Tongtong

引用次数: 13

Redeployment of cluster heads in wireless sensor networks with genetic algorithm 基于遗传算法的无线传感器网络簇头重新部署

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645087

A. Ghaffari, Farhad Nematy, N. Rahmani

引用次数: 5

A network comparison algorithm for predicting the conservative interaction regions in protein-protein interaction network 蛋白质-蛋白质相互作用网络中保守相互作用区域预测的网络比较算法

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645297

Lihong Peng, Lipeng Liu, Shi Chen, Quanwei Sheng

{"title":"A network comparison algorithm for predicting the conservative interaction regions in protein-protein interaction network","authors":"Lihong Peng, Lipeng Liu, Shi Chen, Quanwei Sheng","doi":"10.1109/BICTA.2010.5645297","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645297","url":null,"abstract":"We presented a network comparison algorithm for predicting the conservative interaction regions in the cross-species protein-protein interaction networks (PINs). In the first place, We made use of the correlated matrix to represent the PINs. Then we standardized the matrix and changed it into a unique representation to facilitate to judge whether the subgraphs is isomorphic. Then we proposed a network comparison algorithm based on the correlated matrix, edge-betweenness and the maximal frequent subgraphs mining. We used the tag grath library composed of the multiple PINs as input data and mined the maximal frequent subgraphs in the cross-species PINs by the algorithm. In the second place, we clustered and merged the similar but different and duplicate locally regions according to the similarity between them and the principle of sigle linkage clustering. In the end we analysed the resulting subgraphs and predicted the conservative interaction regions. The results showed the network comparison algorithm based on mining the frequent subgraplhs can be successfully applied to discover the conservative interaction regions, that is, we can find the functional complexes and predict the protein function. Furthermore, we can predict the interaction will exist in the other species when the conservative regions meet or exceed the threshold of minimum support.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129033885","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: 8

QuantWiz: A scalable parallel software package for label-free protein quantification QuantWiz:一个可扩展的并行软件包，用于无标签蛋白质定量

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645126

Junchang Wang, Yunquan Zhang, Xianyi Zhang, Xiangzheng Sun, Q. Sheng

引用次数: 0

Stateless multicounter 5′ → 3′ Watson-Crick automata 无状态多计数器5 '→3 '沃森-克里克自动机

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645263

Ö. Eğecioğlu, László Hegedüs, B. Nagy

引用次数: 7

A genetic algorithm for construction of recognizers of anomalies in behaviour of dynamical systems 构造动态系统异常行为识别器的遗传算法

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645318

D. Kovalenko, V. Kostenko

引用次数: 5

Two-phase biomedical named entity recognition based on semi-CRFs 基于半crfs的两相生物医学命名实体识别

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645108

Li Yang, Yanhong Zhou

引用次数: 14

The application of fixed-length three DNA Segments encoding to maximum flow problem 定长三DNA片段编码在最大流量问题中的应用

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI: 10.1109/BICTA.2010.5645343

Zhou Kang, H. Yufang, Cheng Zhen, Dong Yafei

{"title":"The application of fixed-length three DNA Segments encoding to maximum flow problem","authors":"Zhou Kang, H. Yufang, Cheng Zhen, Dong Yafei","doi":"10.1109/BICTA.2010.5645343","DOIUrl":"https://doi.org/10.1109/BICTA.2010.5645343","url":null,"abstract":"Fixed-length three DNA Segments encoding is brought forward. Therefore, closed circle DNA computing model is extended. The same position of extended closed circle DNA is divided into three sections corresponding to three function areas. The three function areas are addition segment, filling segment and subtraction segment, so extended closed circle DNA computing model can do addition operation and subtraction operation with simultaneous. For maximum flow problem, DNA algorithm is designed based on extended closed circle DNA computing model. In the DNA algorithm, fixed-length three DNA Segments encoding is encoded for flow rate of every arc, and all capacity feasible flows are formed. Then all feasible flows are filtered out by doing group insert experiment, group delete experiment and electrophoresis experiment. Using the same method all maximum flows are filtered out. Finally all maximum flows are obtained by doing detect experiment. Correctness and complexity of the algorithm are analyzed and proved. And a simulation experiment is done to verify validity of the DNA algorithm. This encoding mode is discovered firstly, and it is firstly using DNA computing from beginning to end to thoroughly solve maximum flow problem, so a conclusion can be drawn that the innovation of DNA encoding structure can solve more complicated and more extensive problems by DNA computing.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128602782","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: 0