Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献

Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data. 利用核糖体定位数据发现人类疟疾寄生虫中的新基因

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2010-08-01

N Pokhriyal, N Ponts, E Y Harris, K G Le Roch, S Lonardi

引用次数: 0

Estimating support for protein-protein interaction data with applications to function prediction. 估计蛋白质-蛋白质相互作用数据在功能预测中的应用支持度。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-08-01 DOI: 10.1142/9781848162648_0007

Erliang Zeng, C. Ding, G. Narasimhan, S. Holbrook

{"title":"Estimating support for protein-protein interaction data with applications to function prediction.","authors":"Erliang Zeng, C. Ding, G. Narasimhan, S. Holbrook","doi":"10.1142/9781848162648_0007","DOIUrl":"https://doi.org/10.1142/9781848162648_0007","url":null,"abstract":"Almost every cellular process requires the interactions of pairs or larger complexes of proteins. High throughput protein-protein interaction (PPI) data have been generated using techniques such as the yeast two-hybrid systems, mass spectrometry method, and many more. Such data provide us with a new perspective to predict protein functions and to generate protein-protein interaction networks, and many recent algorithms have been developed for this purpose. However, PPI data generated using high throughput techniques contain a large number of false positives. In this paper, we have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resnik's formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true protein-protein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"73-84"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003189","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: 13

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":"<p><p>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.</p>","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

MSDash: mass spectrometry database and search. 质谱数据库和搜索。

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

Zhan Wu, Gilles Lajoie, Bin Ma

引用次数: 0

Error tolerant sibship reconstruction in wild populations. 野生种群的容错兄弟姐妹重建。

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

Saad I Sheikh, Tanya Y Berger-Wolf, Mary V Ashley, Isabel C Caballero, Wanpracha Chaovalitwongse, Bhaskar DasGupta

引用次数: 0

Iterative non-sequential protein structural alignment. 迭代非顺序蛋白质结构比对。

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

Saeed Salem, Mohammed J Zaki

引用次数: 0

Optimizing Bayes error for protein structure model selection by stability mutagenesis. 基于稳定性诱变的蛋白质结构模型选择贝叶斯误差优化。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0009

Xiaoduan Ye, A. Friedman, C. Bailey-Kellogg

{"title":"Optimizing Bayes error for protein structure model selection by stability mutagenesis.","authors":"Xiaoduan Ye, A. Friedman, C. Bailey-Kellogg","doi":"10.1142/9781848162648_0009","DOIUrl":"https://doi.org/10.1142/9781848162648_0009","url":null,"abstract":"Site-directed mutagenesis affects protein stability in a manner dependent on the local structural environment of the mutated residue; e.g., a hydrophobic to polar substitution would behave differently in the core vs. on the surface of the protein. Thus site-directed mutagenesis followed by stability measurement enables evaluation of and selection among predicted structure models, based on consistency between predicted and experimental stability changes (DeltaDeltaGo values). This paper develops a method for planning a set of individual site-directed mutations for protein structure model selection, so as to minimize the Bayes error, i.e., the probability of choosing the wrong model. While in general it is hard to calculate exactly the multi-dimensional Bayes error defined by a set of mutations, we leverage the structure of \"DeltaDeltaGo space\" to develop tight upper and lower bounds. We further develop a lower bound on the Bayes error of any plan that uses a fixed number of mutations from a set of candidates. We use this bound in a branch-and-bound planning algorithm to find optimal and near-optimal plans. We demonstrate the significance and effectiveness of this approach in planning mutations for elucidating the structure of the pTfa chaperone protein from bacteriophage lambda.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"99-108"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003318","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: 3

Fast and accurate multi-class protein fold recognition with spatial sample kernels. 利用空间样本核快速准确地识别多类蛋白质折叠。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0012

P. Kuksa, Pai-Hsi Huang, V. Pavlovic

引用次数: 4

Consistent alignment of metabolic pathways without abstraction. 一致的排列代谢途径没有抽象。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0021

F. Ay, Tamer Kahveci, V. de Crécy-Lagard

{"title":"Consistent alignment of metabolic pathways without abstraction.","authors":"F. Ay, Tamer Kahveci, V. de Crécy-Lagard","doi":"10.1142/9781848162648_0021","DOIUrl":"https://doi.org/10.1142/9781848162648_0021","url":null,"abstract":"Pathways show how different biochemical entities interact with each other to perform vital functions for the survival of organisms. Similarities between pathways indicate functional similarities that are difficult to identify by comparing the individual entities that make up those pathways. When interacting entities are of single type, the problem of identifying similarities reduces to graph isomorphism problem. However, for pathways with varying types of entities, such as metabolic pathways, alignment problem is more challenging. Existing methods, often, address the metabolic pathway alignment problem by ignoring all the entities except for one type. This kind of abstraction reduces the relevance of the alignment significantly as it causes losses in the information content. In this paper, we develop a method to solve the pairwise alignment problem for metabolic pathways. One distinguishing feature of our method is that it aligns reactions, compounds and enzymes without abstraction of pathways. We pursue the intuition that both pairwise similarities of entities (homology) and their organization (topology) are crucial for metabolic pathway alignment. In our algorithm, we account for both by creating an eigenvalue problem for each entity type. We enforce the consistency by considering the reachability sets of the aligned entities. Our experiments show that, our method finds biologically and statistically significant alignments in the order of seconds for pathways with approximately 100 entities.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"237-48"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003751","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: 16

Error tolerant sibship reconstruction in wild populations. 野生种群的容错兄弟姐妹重建。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0024

S. Sheikh, T. Berger-Wolf, M. Ashley, I. Caballero, W. Chaovalitwongse, B. Dasgupta

引用次数: 19