Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献_第2页

A max-flow based approach to the identification of protein complexes using protein interaction and microarray data. 利用蛋白质相互作用和微阵列数据，基于最大流量的方法来鉴定蛋白质复合物。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Jianxing Feng, Rui Jiang, Tao Jiang

{"title":"A max-flow based approach to the identification of protein complexes using protein interaction and microarray data.","authors":"Jianxing Feng, Rui Jiang, Tao Jiang","doi":"","DOIUrl":"","url":null,"abstract":"The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. Although it has been demonstrated in the literature that methods using protein-protein interaction data alone can successfully predict a large number of protein complexes, the incorporation of gene expression profiles could help refine the putative complexes and hence improve the accuracy of the computational methods. By combining protein-protein interaction data and microarray gene expression profiles, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our extensive tests on three widely used protein-protein interaction datasets and comparisons with the latest methods for protein complex identification demonstrate the superior performance of our method in terms of accuracy, efficiency, and capability in predicting novel protein complexes. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"51-62"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28336172","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

Detecting pathways transcriptionally correlated with clinical parameters. 检测途径转录与临床参数相关。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Igor Ulitsky, Ron Shamir

引用次数: 0

The effect of massive gene loss following whole genome duplication on the algorithmic reconstruction of the ancestral populus diploid. 全基因组复制后大量基因丢失对杨树祖先二倍体算法重建的影响。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Chunfang Zheng, P Kerr Wall, Jim Leebens-Mack, Victor A Albert, Claude dePamphilis, David Sankoff

引用次数: 0

Extensive exploration of conformational space improves Rosetta results for short protein domains. 对构象空间的广泛探索改善了罗塞塔对短蛋白质结构域的结果。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0018

Yaohang Li, A. Bordner, Yuan Tian, Xiuping Tao, A. Gorin

{"title":"Extensive exploration of conformational space improves Rosetta results for short protein domains.","authors":"Yaohang Li, A. Bordner, Yuan Tian, Xiuping Tao, A. Gorin","doi":"10.1142/9781848162648_0018","DOIUrl":"https://doi.org/10.1142/9781848162648_0018","url":null,"abstract":"With some simplifications, computational protein folding can be understood as an optimization problem of a potential energy function on a variable space consisting of all conformation for a given protein molecule. It is well known that realistic energy potentials are very \"rough\" functions, when expressed in the standard variables, and the folding trajectories can be easily trapped in multiple local minima. We have integrated our variation of Parallel Tempering optimization into the protein folding program Rosetta in order to improve its capability to overcome energy barriers and estimate how such improvement will influence the quality of the folded protein domains. Here we report that (1) Parallel Tempering Rosetta (PTR) is significantly better in the exploration of protein structures than previous implementations of the program; (2) systematic improvements are observed across a large benchmark set in the parameters that are normally followed to estimate robustness of the folding; (3) these improvements are most dramatic in the subset of the shortest domains, where high-quality structures have been obtained for >75% of all tested sequences. Further analysis of the results will improve our understanding of protein conformational space and lead to new improvements in the protein folding methodology, while the current PTR implementation should be very efficient for short (up to approximately 80 a.a.) protein domains and therefore may find practical application in system biology studies.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"203-9"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003563","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: 7

Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures. 利用不相交集数据结构的线性系统从缺失数据的谱系中进行有效的单倍型推断。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0026

Xin Li, Jing Li

{"title":"Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures.","authors":"Xin Li, Jing Li","doi":"10.1142/9781848162648_0026","DOIUrl":"https://doi.org/10.1142/9781848162648_0026","url":null,"abstract":"We study the haplotype inference problem from pedigree data under the zero recombination assumption, which is well supported by real data for tightly linked markers (i.e., single nucleotide polymorphisms (SNPs)) over a relatively large chromosome segment. We solve the problem in a rigorous mathematical manner by formulating genotype constraints as a linear system of inheritance variables. We then utilize disjoint-set structures to encode connectivity information among individuals, to detect constraints from genotypes, and to check consistency of constraints. On a tree pedigree without missing data, our algorithm can output a general solution as well as the number of total specific solutions in a nearly linear time O (mn x alpha(n)), where m is the number of loci, n is the number of individuals and alpha is the inverse Ackermann function, which is a further improvement over existing ones. We also extend the idea to looped pedigrees and pedigrees with missing data by considering existing (partial) constraints on inheritance variables. The algorithm has been implemented in C++ and will be incorporated into our PedPhase package. Experimental results show that it can correctly identify all 0-recombinant solutions with great efficiency. Comparisons with other two popular algorithms show that the proposed algorithm achieves 10 to 10(5)-fold improvements over a variety of parameter settings. The experimental study also provides empirical evidences on the complexity bounds suggested by theoretical analysis.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"297-308"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003575","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: 9

Graph wavelet alignment kernels for drug virtual screening. 用于药物虚拟筛选的图小波对齐核。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Aaron Smalter, Jun Huan, Gerald Lushington

引用次数: 0

Designing secondary structure profiles for fast ncRNA identification. 设计用于ncRNA快速鉴定的二级结构谱。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Yanni Sun, Jeremy Buhler

{"title":"Designing secondary structure profiles for fast ncRNA identification.","authors":"Yanni Sun, Jeremy Buhler","doi":"","DOIUrl":"","url":null,"abstract":"Detecting non-coding RNAs (ncRNAs) in genomic DNA is an important part of annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high computational cost when used for search. This cost can be reduced by using a filter to exclude sequence that is unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite recent advances, designing an efficient filter that can detect nearly all ncRNA instances while excluding most irrelevant sequences remains challenging. This work proposes a systematic procedure to convert a CM for an ncRNA family to a secondary structure profile (SSP), which augments a conservation profile with secondary structure information but can still be efficiently scanned against long sequences. We use dynamic programming to estimate an SSP's sensitivity and FP rate, yielding an efficient, fully automated filter design algorithm. Our experiments demonstrate that designed SSP filters can achieve significant speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families, including those with and without strong sequence conservation. For highly structured ncRNA families, including secondary structure conservation yields better performance than using primary sequence conservation alone.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"145-56"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28337240","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

Improving homology models for protein-ligand binding sites. 改进蛋白质-配体结合位点的同源性模型。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0019

Chris Kauffman, H. Rangwala, G. Karypis

引用次数: 12

Graph wavelet alignment kernels for drug virtual screening. 用于药物虚拟筛选的图小波对齐核。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0029

Aaron M. Smalter, Jun Huan, G. Lushington

引用次数: 5

A max-flow based approach to the identification of protein complexes using protein interaction and microarray data. 利用蛋白质相互作用和微阵列数据，基于最大流量的方法来鉴定蛋白质复合物。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0005

Jianxing Feng, Rui Jiang, Tao Jiang

{"title":"A max-flow based approach to the identification of protein complexes using protein interaction and microarray data.","authors":"Jianxing Feng, Rui Jiang, Tao Jiang","doi":"10.1142/9781848162648_0005","DOIUrl":"https://doi.org/10.1142/9781848162648_0005","url":null,"abstract":"The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. Although it has been demonstrated in the literature that methods using protein-protein interaction data alone can successfully predict a large number of protein complexes, the incorporation of gene expression profiles could help refine the putative complexes and hence improve the accuracy of the computational methods. By combining protein-protein interaction data and microarray gene expression profiles, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our extensive tests on three widely used protein-protein interaction datasets and comparisons with the latest methods for protein complex identification demonstrate the superior performance of our method in terms of accuracy, efficiency, and capability in predicting novel protein complexes. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"51-62"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64002534","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: 62