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Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. In addition, a priori biological knowledge from genome-wide location analysis is incorporated into the structure of the gene regulatory network. tdGRN is evaluated on both an artificial dataset and a published gene expression data set...
Source: EURASIP Journal on Bioinformatics and Systems Biology - October 15, 2009 Category: Bioinformatics Source Type: journals

Stochastic Simulation of Delay-Induced Circadian Rhythms in DrosophilaEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Circadian rhythms are ubiquitous in all eukaryotes and some prokaryotes. Several computational models with or without time delays have been developed for circadian rhythms. Exact stochastic simulations have been carried out for several models without time delays, but no exact stochastic simulation has been done for models with delays. In this paper, we proposed a detailed and a reduced stochastic model with delays for circadian rhythms in Drosophila based on two deterministic models of Smolen et al. and employed exact stochastic simulation to simulate circadian oscillations. Our simulations showed that both models can prod...
Source: EURASIP Journal on Bioinformatics and Systems Biology - July 20, 2009 Category: Bioinformatics Source Type: journals

Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation MaximizationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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: activi...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 25, 2009 Category: Bioinformatics Source Type: journals

Reverse Engineering of Gene Regulatory Networks: A Comparative StudyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and B...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 25, 2009 Category: Bioinformatics Source Type: journals

Network Structure and Biological Function: Reconstruction, Modeling, and Statistical ApproachesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: EURASIP Journal on Bioinformatics and Systems Biology)
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 25, 2009 Category: Bioinformatics Source Type: journals

A Time-Series-Based Feature Extraction Approach for Prediction of Protein Structural ClassEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized sup...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Optimal Constrained Stationary Intervention in Gene Regulatory NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Recent advances in high-throughput DNA microarrays and chromatin immunoprecipitation (ChIP) assays have enabled the learning of the structure and functionality of genetic regulatory networks. In light of these heterogeneous data sets, this paper proposes a novel approach for reconstruction of genetic regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, thereby it reduces significantly the ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Detecting Periodic Genes from Irregularly Sampled Gene Expressions: A Comparison StudyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Time series microarray measurements of gene expressions have been exploited to discover genes involved in cell cycles. Due to experimental constraints, most microarray observations are obtained through irregular sampling. In this paper three popular spectral analysis schemes, namely, Lomb-Scargle, Capon and missing-data amplitude and phase estimation (MAPES), are compared in terms of their ability and efficiency to recover periodically expressed genes. Based on in silico experiments for microarray measurements of Saccharomyces cerevisiae, Lomb-Scargle is found to be the most efficacious scheme. 149 genes are then identifie...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Gene Regulatory Network Reconstruction Using Conditional Mutual InformationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gen...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Using Temporal Correlation in Factor Analysis for Reconstructing Transcription Factor ActivitiesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Two-level gene regulatory networks consist of the transcription factors (TFs) in the top level and their regulated genes in the second level. The expression profiles of the regulated genes are the observed high-throughput data given by experiments such as microarrays. The activity profiles of the TFs are treated as hidden variables as well as the connectivity matrix that indicates the regulatory relationships of TFs with their regulated genes. Factor analysis (FA) as well as other methods, such as the network component algorithm, has been suggested for reconstructing gene regulatory networks and also for predicting TF acti...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Inference of Boolean Networks Using Sensitivity RegularizationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The inference of genetic regulatory networks from global measurements of gene expressions is an important problem in computational biology. Recent studies suggest that such dynamical molecular systems are poised at a critical phase transition between an ordered and a disordered phase, affording the ability to balance stability and adaptability while coordinating complex macroscopic behavior. We investigate whether incorporating this dynamical system-wide property as an assumption in the inference process is beneficial in terms of reducing the inference error of the designed network. Using Boolean networks, for which there ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact a...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Towards Systems Biology of Heterosis: A Hypothesis about Molecular Network Structure Applied for the Arabidopsis MetabolomeEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our dat...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Origins of Stochasticity and Burstiness in High-Dimensional Biochemical NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Two major approaches are known in the field of stochastic dynamics of intracellular biochemical networks. The first one places the focus of attention on the fact that many biochemical constituents vitally important for the network functionality may be present only in small quantities within the cell, and therefore the regulatory process is essentially discrete and prone to relatively big fluctuations. The second approach treats the regulatory process as essentially continuous. Complex pseudostochastic behavior in such processes may occur due to multistability and oscillatory motions within limit cycles. In this paper we o...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

The Impact of Time Delays on the Robustness of Biological Oscillators and the Effect of Bifurcations on the Inverse ProblemEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Differential equation models for biological oscillators are often not robust with respect to parameter variations. They are based on chemical reaction kinetics, and solutions typically converge to a fixed point. This behavior is in contrast to real biological oscillators, which work reliably under varying conditions. Moreover, it complicates network inference from time series data. This paper investigates differential equation models for biological oscillators from two perspectives. First, we investigate the effect of time delays on the robustness of these oscillator models. In particular, we provide sufficient conditions ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Integrating Biosystem Models Using Waveform RelaxationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Modelling in systems biology often involves the integration of component models into larger composite models. How to do this systematically and efficiently is a significant challenge: coupling of components can be unidirectional or bidirectional, and of variable strengths. We adapt the waveform relaxation (WR) method for parallel computation of ODEs as a general methodology for computing systems of linked submodels. Four test cases are presented: (i) a cascade of unidirectionally and bidirectionally coupled harmonic oscillators, (ii) deterministic and stochastic simulations of calcium oscillations, (iii) single cell calciu...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

A Bayesian Network View on Nested Effects ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the R/Bioconductor package nem. We validate these methods in...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual InformationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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 f...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Compressive Sensing DNA MicroarraysEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. La...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Assessing the Exceptionality of Coloured Motifs in NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Various methods have been recently employed to characterise the structure of biological networks. In particular, the concept of network motif and the related one of coloured motif have proven useful to model the notion of a functional/evolutionary building block. However, algorithms that enumerate all the motifs of a network may produce a very large output, and methods to decide which motifs should be selected for downstream analysis are needed. A widely used method is to assess if the motif is exceptional, that is, over- or under-represented with respect to a null hypothesis. Much effort has been put in the last thirty ye...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Stability from Structure: Metabolic Networks Are Unlike Other Biological NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we extend a previously proposed method by changing the null model for determining motif enrichment, by using interaction types directly obtained from structural interaction matrices, by generating a distribution of partial derivatives of reaction rates and by simulating enzymatic regulation on metabolic networks. Our findings sugg...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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 ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

How to Improve Postgenomic Knowledge Discovery Using ImputationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputati...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Adaptive Dynamics of Regulatory Networks: Size MattersEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter networ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Functional Classification of Genome-Scale Metabolic NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose two strategies to characterize organisms with respect to their metabolic capabilities. The first, investigative, strategy describes metabolic networks in terms of their capability to utilize different carbon sources, resulting in the concept of carbon utilization spectra. In the second, predictive, approach minimal nutrient combinations are predicted from the structure of the metabolic networks, resulting in a characteristic nutrient profile. Both strategies allow for a quantification of functional properties of metabolic networks, allowing to identify groups of organisms with similar functions. We investigate w...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Spectral Preprocessing for Clustering Time-Series Gene ExpressionsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the tradi...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

A Hybrid Technique for the Periodicity Characterization of Genomic Sequence DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Many studies of biological sequence data have examined sequence structure in terms of periodicity, and various methods for measuring periodicity have been suggested for this purpose. This paper compares two such methods, autocorrelation and the Fourier transform, using synthetic periodic sequences, and explains the differences in periodicity estimates produced by each. A hybrid autocorrelation—integer period discrete Fourier transform is proposed that combines the advantages of both techniques. Collectively, this representation and a recently proposed variant on the discrete Fourier transform offer alternatives to th...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Efficient Alignment of RNAs with Pseudoknots Using Sequence Alignment ConstraintsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
When aligning RNAs, it is important to consider both the secondary structure similarity and primary sequence similarity to find an accurate alignment. However, algorithms that can handle RNA secondary structures typically have high computational complexity that limits their utility. For this reason, there have been a number of attempts to find useful alignment constraints that can reduce the computations without sacrificing the alignment accuracy. In this paper, we propose a new method for finding effective alignment constraints for fast and accurate structural alignment of RNAs, including pseudoknots. In the proposed meth...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Intervention in Context-Sensitive Probabilistic Boolean Networks RevisitedEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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 networ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior KnowledgeEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partia...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Clustering of Gene Expression Data Based on Shape SimilarityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which th...
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Applications of Signal Processing Techniques to Bioinformatics, Genomics, and ProteomicsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: EURASIP Journal on Bioinformatics and Systems Biology)
Source: EURASIP Journal on Bioinformatics and Systems Biology - June 10, 2009 Category: Bioinformatics Source Type: journals

Clustering of Gene Expression Data Based on Shape SimilarityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which th...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 24, 2009 Category: Bioinformatics Source Type: journals

Applications of Signal Processing Techniques to Bioinformatics, Genomics, and ProteomicsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: EURASIP Journal on Bioinformatics and Systems Biology)
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 24, 2009 Category: Bioinformatics Source Type: journals

Spectral Preprocessing for Clustering Time-Series Gene ExpressionsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the tradi...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

A Hybrid Technique for the Periodicity Characterization of Genomic Sequence DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Many studies of biological sequence data have examined sequence structure in terms of periodicity, and various methods for measuring periodicity have been suggested for this purpose. This paper compares two such methods, autocorrelation and the Fourier transform, using synthetic periodic sequences, and explains the differences in periodicity estimates produced by each. A hybrid autocorrelation—integer period discrete Fourier transform is proposed that combines the advantages of both techniques. Collectively, this representation and a recently proposed variant on the discrete Fourier transform offer alternatives to th...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

Efficient Alignment of RNAs with Pseudoknots Using Sequence Alignment ConstraintsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
When aligning RNAs, it is important to consider both the secondary structure similarity and primary sequence similarity to find an accurate alignment. However, algorithms that can handle RNA secondary structures typically have high computational complexity that limits their utility. For this reason, there have been a number of attempts to find useful alignment constraints that can reduce the computations without sacrificing the alignment accuracy. In this paper, we propose a new method for finding effective alignment constraints for fast and accurate structural alignment of RNAs, including pseudoknots. In the proposed meth...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

Intervention in Context-Sensitive Probabilistic Boolean Networks RevisitedEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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 networ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior KnowledgeEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partia...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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...
Source: EURASIP Journal on Bioinformatics and Systems Biology - April 18, 2009 Category: Bioinformatics Source Type: journals

Functional Classification of Genome-Scale Metabolic NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose two strategies to characterize organisms with respect to their metabolic capabilities. The first, investigative, strategy describes metabolic networks in terms of their capability to utilize different carbon sources, resulting in the concept of carbon utilization spectra. In the second, predictive, approach minimal nutrient combinations are predicted from the structure of the metabolic networks, resulting in a characteristic nutrient profile. Both strategies allow for a quantification of functional properties of metabolic networks, allowing to identify groups of organisms with similar functions. We investigate w...
Source: EURASIP Journal on Bioinformatics and Systems Biology - March 18, 2009 Category: Bioinformatics Source Type: journals

Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - March 18, 2009 Category: Bioinformatics Source Type: journals

Adaptive Dynamics of Regulatory Networks: Size MattersEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter networ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - March 14, 2009 Category: Bioinformatics Source Type: journals

How to Improve Postgenomic Knowledge Discovery Using ImputationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputati...
Source: EURASIP Journal on Bioinformatics and Systems Biology - February 8, 2009 Category: Bioinformatics Source Type: journals

Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
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 ...
Source: EURASIP Journal on Bioinformatics and Systems Biology - February 5, 2009 Category: Bioinformatics Source Type: journals

Stability from Structure: Metabolic Networks Are Unlike Other Biological NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we extend a previously proposed method by changing the null model for determining motif enrichment, by using interaction types directly obtained from structural interaction matrices, by generating a distribution of partial derivatives of reaction rates and by simulating enzymatic regulation on metabolic networks. Our findings sugg...
Source: EURASIP Journal on Bioinformatics and Systems Biology - February 1, 2009 Category: Bioinformatics Source Type: journals

Assessing the Exceptionality of Coloured Motifs in NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Various methods have been recently employed to characterise the structure of biological networks. In particular, the concept of network motif and the related one of coloured motif have proven useful to model the notion of a functional/evolutionary building block. However, algorithms that enumerate all the motifs of a network may produce a very large output, and methods to decide which motifs should be selected for downstream analysis are needed. A widely used method is to assess if the motif is exceptional, that is, over- or under-represented with respect to a null hypothesis. Much effort has been put in the last thirty ye...
Source: EURASIP Journal on Bioinformatics and Systems Biology - January 27, 2009 Category: Bioinformatics Source Type: journals