2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献

Degenerate primer selection algorithms 退化引物选择算法

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925722

Dolly Sharma, Sudha Balla, S. Rajasekaran, Nikoletta DiGirolamo

引用次数: 3

Performance prediction for RNA design using parametric and non-parametric regression models 基于参数和非参数回归模型的RNA设计性能预测

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925702

D. C. Dai, K. Wiese

{"title":"Performance prediction for RNA design using parametric and non-parametric regression models","authors":"D. C. Dai, K. Wiese","doi":"10.1109/CIBCB.2009.4925702","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925702","url":null,"abstract":"Empirical algorithm study involves tuning various parameter settings in order to achieve an optimal performance. It is also experimentally known that algorithm performance varies across problem instances. In stochastic local search (metaheuristics) paradigm, search efficiency is correlated to the empirical hardness of the underlying combinatorial optimization problem itself. Therefore, investigating these correlations are of crucial importance towards the design of robust algorithmic solutions. To achieve this goal, an accurate prediction of algorithm performance is a prerequisite, since it allows an automatic tuning of parameter settings on a perproblem base. In this work, we investigate using parametric & non-parametric regression models for algorithm performance prediction for the RNA Secondary Structure Design problem (SSD). Empirical results show our non-parametric methods achieve a higher prediction accuracy on biologically existing data, where biological data exhibits a higher degree of local similarity among individual instances. We also found that using a non-parametric regression tree model (CART) provides insight into studying the empirical hardness of solving the SSD problem.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114567296","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: 1

Protein secondary structure prediction using rule induction from coverings 覆盖物规则归纳法预测蛋白质二级结构

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925711

Leong Lee, J. Leopold, R. Frank, A. Maglia

引用次数: 7

Haplotype inference using a genetic algorithm 利用遗传算法进行单倍型推断

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925704

Dongsheng Che, Haibao Tang, Yinglei Song

引用次数: 2

An assignment walk through 3D NMR spectrum 通过3D核磁共振谱的作业

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925731

Marta Szachniuk, M. Popenda, R. Adamiak, J. Błażewicz

引用次数: 13

Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks 蛋白质相互作用网络中重叠功能模块的快速检测算法

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925735

P. Sun, Lin Gao

{"title":"Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks","authors":"P. Sun, Lin Gao","doi":"10.1109/CIBCB.2009.4925735","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925735","url":null,"abstract":"Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules which is that groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is likely to capture the biologically meaningful interactions. In recent years, many algorithms have been developed for detecting such structures. These algorithms however are computationally demanding, which limits their application. The existing deterministic methods used for large networks find separated modules, whereas most of the actual networks are made of highly overlapping cohesive groups of vertices. In this paper, we propose an iterative-clique percolation method (ICPM) for identifying overlapping modules in PPI (protein-protein interaction) networks. Our method is based on clique percolation method (CPM) which not only considers the degree of nodes to minimize the search space (The vertices in k-cliques must have the degree of k-1 at least), but also converts k-cliques to (k-1)-cliques. It uses (k-1)-cliques by appending one node to (k-1)-cliques for finding k-cliques. Furthermore, since the PPI network is noisy and still incomplete, some methods treat the PPI networks as weighted graphs in which each edge (e.g., interaction) is associated with a weight representing the probability or reliability of that interaction for preprocessing and purifying PPI data. Thus, we extend the ICPM into weighted networks which takes into account the link weights in a more delicate way by incorporating the subgraph intensity. We test our method on both computer-generated and PPI networks. Our analysis of the yeast PPI network suggests that most of these modules have well-supported biological significance in the context of protein localization, function annotation, protein complexes.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131515166","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: 2

Goal Driven Analysis of cDNA Microarray Data cDNA微阵列数据的目标驱动分析

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925727

Youlian Pan, Jitao Zou, Yi Huang, Ziying Liu, Sieu Phan, Fazel Famili

引用次数: 4

Application of machine learning approaches on quantitative structure activity relationships 机器学习方法在定量结构活动关系中的应用

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925736

Mariusz Butkiewicz, Ralf Mueller, Danilo Selic, E. Dawson, J. Meiler

引用次数: 18

An application of the metric access methods to the mass spectrometry data 计量存取方法在质谱数据中的应用

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925732

J. Novák, D. Hoksza

引用次数: 3

Shape modeling and clustering of white matter fiber tracts using fourier descriptors 利用傅立叶描述子的白质纤维束形状建模与聚类

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925741

Xuwei Liang, Qi Zhuang, Ning Cao, Jun Zhang

{"title":"Shape modeling and clustering of white matter fiber tracts using fourier descriptors","authors":"Xuwei Liang, Qi Zhuang, Ning Cao, Jun Zhang","doi":"10.1109/CIBCB.2009.4925741","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925741","url":null,"abstract":"Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors which are effective in capturing shape properties of fiber tracts. Fourier descriptors derived from different shape signatures are analyzed. Clustering is then performed on these multi-dimensional features in conjunction with mass centers using a k-means like threshold based approach. The advantage of this method lies in the fact that Fourier descriptors achieve spatial independent representation and normalization of white matter fiber tracts which makes it useful for tract comparison across subjects. It also eliminates the need to find matching correspondences between two randomly organized tracts from whole brain tracking. Several issues related to tract shape representation and normalization are also discussed. Real DTI datasets are used to test this technique. Experiment results show that this technique can effectively separate multiple fascicles into plausible bundles.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129624677","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: 21