EURASIP journal on bioinformatics & systems biology最新文献_第10页

Stability from structure: metabolic networks are unlike other biological networks. 结构稳定性:代谢网络不同于其他生物网络。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 DOI: 10.1155/2009/630695

P van Nes, D Bellomo, M J T Reinders, D de Ridder

引用次数: 4

Using a state-space model and location analysis to infer time-delayed regulatory networks. 使用状态空间模型和位置分析来推断时滞调节网络。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 Epub Date: 2009-10-15 DOI: 10.1155/2009/484601

Chushin Koh, Fang-Xiang Wu, Gopalan Selvaraj, Anthony J Kusalik

引用次数: 20

Adaptive dynamics of regulatory networks: size matters. 调节网络的自适应动态:大小问题。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 Epub Date: 2009-03-12 DOI: 10.1155/2009/618502

Dirk Repsilber, Thomas Martinetz, Mats Björklund

引用次数: 0

On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information. 熵估计对基于互信息的转录调控网络推断的影响。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 DOI: 10.1155/2009/308959

Catharina Olsen, Patrick E Meyer, Gianluca Bontempi

{"title":"On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.","authors":"Catharina Olsen, Patrick E Meyer, Gianluca Bontempi","doi":"10.1155/2009/308959","DOIUrl":"https://doi.org/10.1155/2009/308959","url":null,"abstract":"The reverse engineering of transcription regulatory networks from expression data is gaining large interest in the bioinformatics community. An important family of inference techniques is represented by algorithms based on information theoretic measures which rely on the computation of pairwise mutual information. This paper aims to study the impact of the entropy estimator on the quality of the inferred networks. This is done by means of a comprehensive study which takes into consideration three state-of-the-art mutual information algorithms: ARACNE, CLR, and MRNET. Two different setups are considered in this work. The first one considers a set of 12 synthetically generated datasets to compare 8 different entropy estimators and three network inference algorithms. The two methods emerging as the most accurate ones from the first set of experiments are the MRNET method combined with the newly applied Spearman correlation and the CLR method combined with the Pearson correlation. The validation of these two techniques is then carried out on a set of 10 public domain microarray datasets measuring the transcriptional regulatory activity in the yeast organism.","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":" ","pages":"308959"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2009/308959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9425662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 73

Compressive sensing DNA microarrays. 压缩传感DNA微阵列。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 Epub Date: 2009-01-13 DOI: 10.1155/2009/162824

Wei Dai, Mona A Sheikh, Olgica Milenkovic, Richard G Baraniuk

引用次数: 79

Towards systems biology of heterosis: a hypothesis about molecular network structure applied for the Arabidopsis metabolome. 走向杂种优势的系统生物学:拟南芥代谢组的分子网络结构假说。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 DOI: 10.1155/2009/147157

Sandra Andorf, Tanja Gärtner, Matthias Steinfath, Hanna Witucka-Wall, Thomas Altmann, Dirk Repsilber

引用次数: 12

Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization. 模拟转录调节与因子分析和变分贝叶斯期望最大化的混合物。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 DOI: 10.1155/2009/601068

Kuang Lin, Dirk Husmeier

{"title":"Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization.","authors":"Kuang Lin, Dirk Husmeier","doi":"10.1155/2009/601068","DOIUrl":"https://doi.org/10.1155/2009/601068","url":null,"abstract":"Understanding the mechanisms of gene transcriptional regulation through analysis of high-throughput postgenomic data is one of the central problems of computational systems biology. Various approaches have been proposed, but most of them fail to address at least one of the following objectives: (1) allow for the fact that transcription factors are potentially subject to posttranscriptional regulation; (2) allow for the fact that transcription factors cooperate as a functional complex in regulating gene expression, and (3) provide a model and a learning algorithm with manageable computational complexity. The objective of the present study is to propose and test a method that addresses these three issues. The model we employ is a mixture of factor analyzers, in which the latent variables correspond to different transcription factors, grouped into complexes or modules. We pursue inference in a Bayesian framework, using the Variational Bayesian Expectation Maximization (VBEM) algorithm for approximate inference of the posterior distributions of the model parameters, and estimation of a lower bound on the marginal likelihood for model selection. We have evaluated the performance of the proposed method on three criteria: activity profile reconstruction, gene clustering, and network inference.","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":" ","pages":"601068"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2009/601068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9433271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is bagging effective in the classification of small-sample genomic and proteomic data? bagging在小样本基因组和蛋白质组学数据分类中有效吗?

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 DOI: 10.1155/2009/158368

T T Vu, U M Braga-Neto

{"title":"Is bagging effective in the classification of small-sample genomic and proteomic data?","authors":"T T Vu, U M Braga-Neto","doi":"10.1155/2009/158368","DOIUrl":"https://doi.org/10.1155/2009/158368","url":null,"abstract":"There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":" ","pages":"158368"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2009/158368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9786272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Transition dependency: a gene-gene interaction measure for times series microarray data. 过渡依赖:时间序列微阵列数据的基因-基因相互作用测量。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 Epub Date: 2009-02-05 DOI: 10.1155/2009/535869

Xin Gao, Daniel Q Pu, Peter X-K Song

{"title":"Transition dependency: a gene-gene interaction measure for times series microarray data.","authors":"Xin Gao, Daniel Q Pu, Peter X-K Song","doi":"10.1155/2009/535869","DOIUrl":"https://doi.org/10.1155/2009/535869","url":null,"abstract":"Gene-Gene dependency plays a very important role in system biology as it pertains to the crucial understanding of different biological mechanisms. Time-course microarray data provides a new platform useful to reveal the dynamic mechanism of gene-gene dependencies. Existing interaction measures are mostly based on association measures, such as Pearson or Spearman correlations. However, it is well known that such interaction measures can only capture linear or monotonic dependency relationships but not for nonlinear combinatorial dependency relationships. With the invocation of hidden Markov models, we propose a new measure of pairwise dependency based on transition probabilities. The new dynamic interaction measure checks whether or not the joint transition kernel of the bivariate state variables is the product of two marginal transition kernels. This new measure enables us not only to evaluate the strength, but also to infer the details of gene dependencies. It reveals nonlinear combinatorial dependency structure in two aspects: between two genes and across adjacent time points. We conduct a bootstrap-based chi(2) test for presence/absence of the dependency between every pair of genes. Simulation studies and real biological data analysis demonstrate the application of the proposed method. The software package is available under request.","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":" ","pages":"535869"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2009/535869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27992495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Intervention in context-sensitive probabilistic Boolean networks revisited. 重新审视上下文敏感概率布尔网络中的干预。

EURASIP journal on bioinformatics & systems biology Pub Date : 2009-01-01 Epub Date: 2009-04-15 DOI: 10.1155/2009/360864

Babak Faryabi, Golnaz Vahedi, Jean-Francois Chamberland, Aniruddha Datta, Edward R Dougherty

{"title":"Intervention in context-sensitive probabilistic Boolean networks revisited.","authors":"Babak Faryabi, Golnaz Vahedi, Jean-Francois Chamberland, Aniruddha Datta, Edward R Dougherty","doi":"10.1155/2009/360864","DOIUrl":"https://doi.org/10.1155/2009/360864","url":null,"abstract":"An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters.","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":" ","pages":"360864"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2009/360864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28141504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38