- Open Access
- Open Peer Review
Computational dynamic approaches for temporal omics data with applications to systems medicine
© The Author(s). 2017
- Received: 6 November 2016
- Accepted: 2 June 2017
- Published: 17 June 2017
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.
- Temporal omics data
- Dynamic approaches
- Trajectory prediction
- Causal network
- Systems medicine
- Computational dynamic approaches for temporal omics data with applications to systems medicine
Recent advancement in the omics fields (i.e., genomics, transcriptomics, variomics, proteomics, metabolomics, and interactomics) and the associated technologies (from microarray. RNA sequencing, whole genome sequences (WGS), mass spectrometry (MS)) have provided huge amount of information for delineating the roles of biological entities (i.e., gene mutants, DNA methylations, metabolites) in complex diseases and biological system states for the human organisms [1–7]. On the other hand, the systems and precision medicine known as P4 medicine - Predictive, Preventive, Personalized and Participatory, have been hot topics given the amount of big omics data and knowledge accumulated in the past decades from translational medicine and human genomic/proteomic research [8–11]. In systems medicine, the human organism is envisioned as a system of systems or network of networks, which is hierarchically and biologically organized from genomic/proteomic to molecular, to cellular, to organ, to individual human body, to social/environmental human systems. At each level/scale, those are dynamically embedding each other (as opposed to being static) [8–11].
Despite considerable computational and statistical efforts over the decades with thousands of computational tools, algorithms and models developed ranging from single model to multi-level (such as meta-frame), the key computational challenges of system medicine remains: how to best mine and learn the continuing arrival of big omics data given thousands of interacting entities (e.g., genes or proteins) with relatively weak or small accumulative effects over time on health conditions or diseases [12–14]. The overwhelming number of confounded traits or highly correlated phenotypes with the unavoidable measurement noises makes the integrations even harder, not just metadata or models, but also the results. Moreover, the different topological characteristics of the biological omics data require different sets of algorithms and models (i.e., supervised versus unsupervised; generative or discriminative) for deriving meaningful interpretable relationships. Nevertheless, omics data aggregations, linkage, curation, validation issues from diverse platforms, software outputs, inconsistent data standardizations make clinical implementations harder [15–17]. In addition, analyzing and processing too much combined large data may cause over fitting issues, too complex unstable model, sacrificing predictive accuracy.
To tackle those dynamic, interacting, hidden but valuable biomedical information, various analytical tools ranging from single level to more sophisticated hybrid data mining, machine learning tools, and advanced statistical models are needed, especially the advanced approaches for causal network inferences and dynamic trajectory predictions for drug and disease responses [5, 6, 8–11]. This paper focuses on the various trajectory and interaction approaches for temporal omics data, ranging from single level to multilevel network/cloud computing. These approaches can be either model based (statistical, mathematical, neural network (NN)) or algorithm based (machine learning or data mining) or hybrid ensemble approaches (i.e. with knowledge integration). The examples and recently developed computing tools/resources for comparing various trajectory and interaction methods regarding the merits and drawbacks use the same data sets or different data sets are presented. More applications to pathway, regulations, function, and integrative meta-analysis for various human health, conditions, and diseases are given special attention. Other potentials for future directions (intelligent approaches with deep learning, automatic reasoning; consensus predictions with boosts and bagging) are discussed.
Computational apparoches for temporal experiments
Comparison of the dynamic modeling approaches for temporal omics data, the detailed methods, mining tasks, and type of problems, examples, and related references
Type of problems, tasks
Important features and functions
Math based Deterministic, static
Differential equations, Fourier transform, topology based matrix factorization
Stochastic differential equations, Gaussian graphical models, Probabilistic Boolean networks
State space model and or hidden Markov model, Markov random fields
Parameter/rate estimations, network inference, prediction, time course (I-III)
Dynamic parameter estimations
Causal or non-causal temporal relationships
Fixed, stable parameter, structure estimation, time invaried, non-causal
Nonlinear or linear.
Probabilistic time varied,
Nonlinear or linear
Direct or indirect relationship time course (I-III)
Regression vector autoregressive (VAR) models, Curve fitting, spline methods, Granger causality
Bayesian models (linear or nonlinear model), growth model
Parameter estimations, predictions, hypothesis testing, biomarker/target identifications
Explanatory relationship without prior knowledge, pure data based time course (I-III) or phenotype dependent (IV)
With prior or empirical
Computer sciences based
Machine learning, data mining
Distance or correlation based
Supervised classification with wrapper
Feedback Forward NN, time recurrent NN, convolution NN, Bayesian NN
Subtypes, modular, and heterogeneity discovery, Pattern discovery and identification
Dynamic changes and trajectories
Complex relationship, structure
Time course (I-III) or phenotype dependent (IV)
Without knowing the outcome, classes,
Defined outcomes/classes conditional joint analysis
Time varied or invariaed
Nonlinear or linear
Direct or indirect relationship,
Explanatory or predictive
time course (I-III) or
phenotype dependent (IV)
Interactions and network, pathway function based
Predictions, integrated with public databases
phenotype dependent (IV), Graphic based
Direct or indirect relationship,
Nonlinear or linear
integrated with public databases
interactive through manually
Mathematical modeling: discrete static versus continuous dynamic approaches
The synergistic system formalism is a static differential equation based deterministic approach that has been applied to genetic, immune and biochemical network data [23–25]. Nonlinear discrete dynamical systems also have been developed and applied for temporal data analysis [25–28]. As an example, discrete Boolean networks are developed as probabilistic models of gene regulatory interactions. The corresponding networks are able to cope with uncertainty in order to discover the relative sensitivity of gene-gene interactions [26, 27]. These systems are non-linear and many advanced computational algorithms such as genetic algorithms and linear programming have been implemented for time courses of gene expression. Such types of deterministic interaction models can potentially provide valuable quantitative and mechanistic descriptions of gene activities that may be mediated by drugs and pharmacological agents. However, these traditional mathematical models have not incorporated the stochastic nature of biological process; the time delay or order information and they often treat the biological parameters as fixed values and model them in deterministic ways involved in the estimations.
Singular value decomposition has also been developed for modeling the dynamics of microarray experimental data through matrix decomposition and eigenvalue analysis [29, 30]. The difficulties of these methods are the estimation of the dimensionality of large matrix with ill-posed problems due to large p small n. Dynamic matrix-variate graphical models have demonstrated promising results for dynamic genetic network constructions, have applied for identification of age-related patterns in a public, prefrontal cortex gene expression dataset [31–35]. Topology network and graph based multi-scale approaches decompose the network into subsystems (such as modules and pathways) utilizing various metric measures , which could be further used for predicting the specific functions or phenotypes.
Stochastic paradigm treats the dynamic process of temporal change as a stochastic process and describes it as a probability system in time with uncertainty [36–40]. Examples of stochastic processes are Gaussian process, Markov process, and point process. The advantage of using a stochastic process is that it accounts for the temporal information in the model. The drawback is that it makes some assumptions to model the process, which may not be valid. Chen and colleagues (2005) combine the stochastic process with differential equation and developed a stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae .
State space model (dynamic linear models) and hidden Markov model are two important applications of statistical models combined with stochastic process techniques. State space model combines the stochastic process with the observation data model uniformly to model a continuous process for capturing the change of gene states . Hidden Markov model can be used to model the gene activity systems in which the gene states are unobservable, discrete, but can be represented by a state transition structure determined by the state parameters and the state transition matrix while processing the patterns over time .
State space models have greater flexibility in modeling non-stationary and nonlinear short time course data and were implemented and applied to genomic studies . However, some existing algorithms for these models were based on standard Kalman filter methods, which rely on the linear state transitions and Gaussian errors. Perrin et al. used a penalized likelihood maximization implemented through an extended version of EM algorithm to learn the parameters of the model . Rangel, et al. used classical cross-validations and Bootstrap techniques and Beal et al. used variation approximations with linear time invariant Gaussian setting for constructions of the regulatory network .
Statistical approaches: frequentist versus Bayesian methods
The choice of statistical modeling approaches for temporal omics data depends on the features and types of the data (univariate (I), multivariate (II), cycling (III), phenotype dependent (IV), Fig. 3). The statistical approaches also depend on the scale of the observed outcomes (continuous, discrete: ordinal, binary) and the structure of the balanced or unbalanced data (i.e., diseases type with much more sample than the comparison sample). The associated analysis can be 1) analysis of univariate time course (I) in which each outcome/condition is analyzed separately; 2) Using a joint multivariate modeling strategy for time course II and III for a) assessing the relation between some covariate and all temporal outcomes simultaneously; b) studying how the association between the various temporal outcomes evolves over time; c) investigating the associations among the evolutions of all temporal outcomes and correlated phenotypes, (e.g., periodical or cycling expression data, time course IV).
Moreover, they are also related to the way the association between and across outcomes is modeled (i.e., with or without latent variables); or how the effects of the variables are treated (random, fixed). So the related approaches can be categorized into classical frequentist inferential approaches (fixed effects), Bayesian models (random effects), or mixed of the classical inferential techniques and Bayesian model, which lead to mixed models [44, 45] (see Fig. 3).
Conventional time series techniques such as autoregressive or moving averaging models and Fourier analysis require stationary conditions, linearity for lower order autoregressive models, and uniformly spaced distributed time points, which are not present in short time course omic experiments and therefore are not suitable for unevenly spaced or distributed omic experiment [46, 47]. The repeated analyses of variances (ANOVA), Generalized estimation equation (GEE) or generalized linear mixed models have been applied to time course microarray data. They can model the nonlinear relations between genes, deal with the unevenly time spaced data, and may produce a good fit. But they do not fascinate prediction and may cause over-fitting problems. In addition they do not include the time order information. Functional data analysis methods have been applied to model the temporal data as linear combinations of basis functions (spines) [48, 49].
The probability and confidence measures play important roles in omic temporal data not only due to the variations, high noise levels and experimental errors resident in the experiments but also the stochastic nature involved in the biological process. The Bayesian paradigm is very well suited for examining these features and other properties in the temporal data, such as highly correlated inputs (genes, time points) and phenotypes, missing data, and small sample size [50–56]. In Bayesian models, the parameters are assumed to be random variables and they are associated with some probability distribution, and the posterior probability of these parameters can be expressed as marginal distribution of those remaining parameters.
Moreover, Bayesian approaches can account for the variability induced by the collection of models and construct credible intervals accounting for model uncertainty through investigating the impact of the choice of priors on model space. Then they can construct new search algorithms that take advantage of parallel processing with Markov Chain Monte Carlo (MCMC) algorithm. Bayesian approaches can be used in the case when there are more covariates than observations. Bayesian method is a hybrid generative-discriminative model that can add prior knowledge (such as distributions of the input) or encode the domain knowledge to improve the learning or training phases. Bayesian approaches can well capture linear, non-linear, combinatorial, and stochastic types of relationships among variables across multiple levels of biological organization and have been extensively applied for the time course gene expression study with various hierarchical settings [41, 42, 47–57].
Computer sciences approaches
Machine learning: unsupervised learning versus supervised classifications
Clustering analyses or unsupervised learning without class labels are the most commonly used methods for time course genomic experiments. These approaches are based on similarity or correlation or distance measures for identification of groups of genes with ‘similar’ temporal patterns of expression, which is a critical step in the analysis of kinetic data given the large number of genes involved [58–66]. Hierarchical clustering with heat map, principal component analysis with scatter plots, or dynamic Bayesian clustering (DBC) approaches are a few popular examples [26–28]. DBC can uncover the underlying temporal structure and enable cluster memberships to change for better understanding the development of complex biological organisms and systems .
Supervised clustering or classification approaches incorporate known disease status or the prior known genomic knowledge (e.g., functional annotation tools or publications) as class labels for classifying the genomic temporal patterns and disease/health outcomes [66–70]. Support vector machines (SVM), generalized linear model, discriminant analysis, decision tree, random forest, or neural network are popular examples, which were applied to time course genomic experiments. Semi-supervised learning considers the problem of classification when only a small subset of the observations has corresponding class labels. Vibrational approximations or stochastic variational inference algorithm for semi-supervised learning have also been explored with the omics data and have shown an improved predictive accuracy for the disease/clinical outcomes [71, 72].
Discriminative compared with generative approaches
Classification or supervised clustering approaches can be also distinguished as either generative versus discriminative models. Generative approaches learn the joint probability of inputs x (e.g., genes) and output class label y (e.g., normal versus disease status), then make prediction based on the conditional probability obtained through Bayes rules. Naïve Bayesian classifier is a simple example of generative approaches  while Bayesian or Gaussian mixture models are more sophisticated ; while discriminative approaches directly estimate the conditional probability and learn the direct mapping between the input x to class label y, which is preferred due to many compelling reasons, and a popular example is logistic regression model.
Neural network (feed forward NN, convolutional NN, Bayesian NN) is popular computational approach for prediction problems, which can either apply discriminative or generative strategies [70, 71, 74]. Both unsupervised (i.e., self-organized map) and supervised NN (hierarchical Bayesian NN) have been applied to temporal genomic data for pattern/disease subtype discovery/identifications, or disease classifications/predictions. Neural network with traditional incremental learning and gradient descent algorithms have good classification performance, but such algorithms could be trapped by local minimum solutions when one optimizes a performance/score function, i.e., optimizing the expected reward or minimizing loss functions. In order to find the global optimal solution, recent developed deep learning approach on convolutional NN uses higher order derivative of score functions to obtain the higher order of moments for global optimizations that can handle the convex and local trap issues that may cause misclassifications .
Advanced network and module based approaches
The computational or statistical approaches for network construction include various levels such as transcriptional regulation network, metabolic network, protein-protein, and disease-drug-genes network [76–88]. Networks and module-based approaches reveal hidden patterns in the original unstructured data by transforming raw temporal data into logically structured, clustered, and interconnected graphs [89–95]. These graphs can be visualized with nodes representing genes, proteins and metabolites, and with edges indicating interactions, the potential causal relationships between biological entities (i.e., genes/proteins) or clusters that share similar molecular functions [96–103].
For instance, weighted correlation network analyses identify modules/clusters of highly correlated transcripts, genes, proteins, metabolites [104, 105]. Bayesian network approaches utilize and integrate prior biological domain knowledge (e.g., biochemical pathways, biological processes) with omics data to estimate probabilistic interactions for pathway and biochemical ontology-based integration [106–112]. Friedman and co-workers have used static Bayesian networks, which are graph based models of joint multivariate probability distributions that assess conditional independence between variables. The network obtains simpler sub-models to describe gene interactions from micorarray data . Kimm et al. developed an algorithm to identify interaction network and coupled it with non-parametric regression methods .
Dynamic Bayesian networks (DBN) have been popular for learning and inferring the gene regulatory networks, which have been compared with Granger causality and probabilistic Boolean network [43, 106–109, 114–116]. DBN was also combined with other techniques such as Bayesian regularization in order to handle the non-homogeneous, non-stationary and gradually time-varying structure of time course omics data [106, 116].
For examining the potential causal relationships and network structure, autoregressive models for gene regulatory network inference using time course data for sparsity, stability and causality were investigated . Granger causality approach have been developed for genetic network constructions, and applied for measuring the predictive causality of temporal data [57, 114, 118–122]. Furqan and Siyal proposed the LASSO-based Elastic-Net Copula Granger causality for biological network modeling . Their proposed method shows the merits of overcoming high dimensionality issues of ordinary least-squares methods and linear constraints. Marinazzo et al. (2015) propose a kernel Granger causality method for dynamical networks. They address both the nonlinearity (choosing the kernel function) and false causalities issues (selection strategy of the eigenvectors of a reduced Gram matrix). The results showed that the proposed method is a better choice than using L1 minimization methods . However, Granger causality does not account for latent confounding effects and may not be able to capture instantaneous causal relationships [118, 119].
To investigate the dynamic aspects of gene regulatory networks measured through system variables at multiple time points, Acerbi et al. (2014) proposed continuous time Bayesian networks for network reconstruction. They compared two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis . Results showed that continuous time Bayesian networks were effective on networks of both small and large size, and were particularly feasible when the measurements were not evenly distributed over time. They applied to the reconstruction of the murine Th17 cell differentiation network, and revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.
Pathway and function integrative approaches
Two general categories for data integrations are either through meta-analysis (e.g., Venn diagram), which performs analysis for each individual dataset first, then combines the results; or mega-analysis, which combines the data first then conducts the analysis. No matter which strategy, for better interpretations and visualization purposes, pathway and functional analysis need be conducted. The pathway based analysis move to next level of analysis (complementary to the DAVID and KEGG) to define how the selected individually regulated genes, transcripts, or metabolites interact as parts of complex pathways, such as signaling, metabolic pathways based on known knowledge and published literature [124–126].
For instance, using Ingenuity Pathway Analysis software (http://www.ingenuity.com/) that computes a score for each network according to the fit of the network, one can select a cut-off score of 3 for identifying gene networks significantly affected by the specific gene or genotypes. This score indicates that there is a 1/1000 chance that the genes are in a network due to random chance and therefore, scores of 3 or higher have a 99.9% confidence of not being generated by random chance alone. Then one may compare the selected pathways and networks between DEG lists obtained from individual comparisons (allele carrier vs. not) to find the common and unique pathways between each compartment. These comparisons will indicate the difference of specific genes at the pathway level in addition to our biological process and molecular function analyses, pinpointing the relationship among potential candidate driver genes, chromosomal abnormalities, and pathways.
However, biological pathways are inherently complex and dynamic, pathway annotations in different pathway databases vary significantly in pathway models and in a number of other aspects. For instance, specific protein forms, dynamic complex formation, subcellular locations, and pathway cross talks. Interpretation of pathway mapping results from the fact that pathway annotations currently take little consideration of tissue/urine/serum specificities of genes or proteins in the pathway, thus, specific steps of a pathway may not be actually active in tissues/cells from which the data may be generated which is a limitation.
Further function over-representation analysis through the Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov) identify modules and entities that are enriched and statistically significant over-representation of particular functional categories and major gene/metabolites groups/families [83, 84, 127–129]. Combining with other enrichment and function analysis can facilitate biological interpretation to interrogate complex biological systems for more accurate P4 outcomes [85, 130].
DREAM (The Dialogue for Reverse Engineering Assessment and Methods project (http://www.the-dream-project.org/) provided excellent examples for temporal omics data sets that involve various most updated biomedical challenge questions (e.g. regulatory network inference, causal inferences, dynamic trajectory predictions) through multiple team competitions [16, 17, 86, 118, 119, 131, 132]. For instance, in DREAM 8 (breast cancer network inference challenge), four breast cancer cell lines were stimulated (under inhibitor perturbations) with eight ligands, which comprised of protein abundance time-courses (from 0 min, to, 5, 15, 30, 60, 120, 240 min) for inferring causal signaling networks and predicting trajectory of protein phosphorylation dynamics in cancer . Inferring a causal network is extremely challenging, which significantly differs from association or correlation network. Constructing the dynamical models that can predict trajectories under specific biological perturbations lead to different signaling responses in different backgrounds is also nontrivial task. Results suggest that learning causal relationships may be feasible in complex settings, such as disease states and incorporating known biology was generally advantageous. For drug prediction challenge, the hybrid, Bayesian multitask approaches, which combines nonlinear regression, multiview learning, multitask learning and Bayesian inference (using prior biological knowledge) has showed best performance for predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines .
Furqan and Siyal (2016) utilized silico temporal gene expression data sets from DREAM4 for inferring network structures and predicting the response of the networks to novel perturbations in an optional “bonus round” . They proposed bi-directional Random Forest Granger causality using the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. The ensembing approach was applied to HeLa cell dataset to map gene network involved in cancer . From another study, Marinazzo et al. applied Kernel Granger causality using the same data set with 94 genes and 48 time points. Results showed evidence of 19 causal relationships, all involving genes related to tumor development .
Eren et al. (2015) developed an advanced automated and human-guided characterization and visualization platform for microbial genomes in metagenomic assemblies. The platform has interactive interfaces that can link omics data from multiple sources into a single, intuitive display . The software includes multi-levels from data preprocessing (i.e., merging, profiling), to unsupervised and supervised learning, hidden Markov model for metagenomics shot read RNA-seq data. They analyzed time course infant gut metagenomes data set (at days 15–19 and 22–24 after birth), and explored temporal genomic changes within naturally occurring microbial populations through de novo characterization of single nucleotide variations. They also linked those with cultivar and single-cell genomes with metagenomic and metatranscriptomic data. They identified systematic emergence of nucleotide variation in an abundant draft genome bin in an infant’s gut. Other applications to different common disease and health conditions by integrations of temporal omics data ranged from single cell analysis to multiple tissues/organs and have been extended by leveraging to social environmental interactions [88, 133–147].
In addition, they provide a dynamic interface (Grinn) to integrate gene, protein, and metabolite data using more advanced biological-network-based approaches such as Gaussian graphical models, partial correlation and Bayesian networks for omics data integration (glasso, qpgraph). For instance, time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) is an R package built to extract regulatory sub-pathways along with their miRNA regulators at each time point based on KEGG pathway maps and user-defined time series mRNA and microRNA (if available) expression profiles for microarray experiments [156, 157]. It can assist significantly in complex disease analysis by enabling the experimentalists to shift from the dynamic to the more realistic time-varying view of the involved perturbed mechanisms. NSPEcT is based on differential equation that describes the process of synthesis and processing of pre-mRNA and the degradation of mature mRNA. It’s a package used for estimation of total mRNA levels, pre-mRNA levels, and degradation rates over time for each gene (from time course RNA-seq) . Furthermore, NSPEcT can test different models of transcriptional regulation to identify the most likely combination of rates explaining the observed changes in gene expression.
Temporal omic data software, libraries and packages, tools and web resources ranged from fundamental data preprocessing, immediate analysis to advanced network and pathway and integration analysis
Omics variety data, formats
Various types of genomic data from case-control, SNPs, RNA seq…
Quality-control tools including batch effect removal, PCA, ANOVA, differential analysis, cluster, and prediction e.g., Grinn, MetaMapR, glasso, qpgraph
Gene-expression, exon-expression, proteins
Neural network, math optimization modeling, nonlinear dynamic systems; prediction,Multidimensional data visualization, Statistical/machine
All types of omics data, More than 1200 packages, annotation, experiments, explore, analyze, visualize,
Quality assurance analysis, normalizationVarious statistical (including Bayesian modeling) and algorithm based tools, Cloud-enabled
Qlucore Omics Explorer
RNA seq, microarray, miRNA, Methylation, MS for proteins and metabolites, and Flow cytometry data
Visualization, and biological interpretation; view on the chromatograms; Integration with proteomic and metabolomic data, Automated quality and pre-processing, Standardized workflows
Exon gene level Microarray, NGS, Protein, RNA-Seq, SNP Metagnomics, chip to chip
Visualizing and Comparing, Multiple Genome-Scale Assemblies modelling and simulation of regulatory networks
miRNA Activity, Molecule interactions DNA proteins interactions
Topology-based Analysis Advanced Correction Factors Prediction, Downstream Impact Analysis
Genes, microRNAs, pathways, biological processes, molecular functions, cellular components, drugs, diseases,
find related genes, pathways, biological processes, molecular functions, cellular components, drugs, diseases,
Genotyping™ Console Software
Clinical Genome nClinGen/ClinVar)
SNP, copy number variation, SNP genotyping, indel detection
Analyze rare and common variants
Genotyping calls, loss of heterozygosity; Dynamic Graphic Filters Pathway Analysis GO Terms Analysis Cloud-based Sharing, Data management and a data repository
The Rat Genome Database pathway diagrams
Molecular and physiological pathway; e.g., identifying up or down regulated genes in pathways, see how pathways relate to each other
Pathway acquisition and visualization, multi-layered approach, dynamic and integrated manner, interactive diagram
Next Generation Sequencing Data, XRAY or other expression, FASTA, FASTQ
Excel plug-in interfaces, Integrated annotations, Illumina Genome Analyzer Pipeline
Learning and integrating dynamic omics temporal data and gene-protein-disease-drug/treatment correlation, interdependence and causal networks between hybrid systems may improve our understanding of system-wide dynamics and errors of pharmacological and biomedical agents and their genetic and environmental modifiers. Most available dynamic approaches and existing applications focus on the genomic time course data, but the same techniques or methodologies can be extended and employed to various types of omics data (such as metagenomics) with the applications to other biological networks and pathways. For instance, RNA-Seq data has revealed far more about the transcriptome than microarrays, primarily because analysis is not limited to known genes. This opens possibly for splicing analysis, analyzing differential allele expression, variant detection, alternative start/stop, gene fusion detection, RNA editing and eQTL mapping.
Either from computational complexity or clinical reproducibility point of view, one cost effective resolutions and future directions would be develop more intelligent AI based data integrations, learning and automations with hierarchical ensemble approaches, not just connectivity. With efficient multi-task learning algorithms (with automatic reasoning and consensus predictions with boosts and bagging) embedded into multilayer computational automated ensemble model systems with pipelines, the latent component of correlated biological entities can be divided and the key components/pathway or elements can be captured through utilizing continuously arriving, evolving, temporal omics data. Investigating the causality rather than the association among various biological entities ranging from RNA, microRNA, DNA, gene, protein, disease, and drug in an integrative perspective would be important, to which relative a few integrative efforts have been dedicated so far.
To overcome other bottleneck issues for omics data that may partially arisen from the biomedical systems’ complexity, that encompasses biological/genetic, behavioral, psychosocial, societal, environmental, systems-related, ethical and other intertwined factors. Further incorporations of electronic health records linked to behavioral, psychosocial, societal, environmental, and clinical lab measures with temporal omics data in hierarchical ensemble automated system will provide us more interpretable and reproducible scientific results and practical clinical decision making for P4 patient outcomes.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Both authors contributed to all aspects of this research. All authors read and approved the final manuscript.
About the Authors
Yulan Liang is an associate professor at the University of Maryland. She holds a Ph.D. in biostatistics. She published extensively in the biostatistics, bioinformatics, statistical genomics and methodological fields for biomedical research with applications to common diseases and health conditions.
Arpad Kelemen is an associate professor of informatics at the University of Maryland. He holds a Ph.D. in computer science. He published extensively in biomedical informatics, health informatics, and computer science, primarily about software design, development, application, evaluation, and usability.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- McElheny, V. K. (2010). Drawing the Map of Life: Inside the Human Genome Project. Basic Books. ISBN 978-0-465-03260-0.Google Scholar
- Tieri P, de la Fuente A, Termanini A, et al. Integrating omics data for signaling pathways, interactome reconstruction, and functional analysis. (2011). Methods Mol Biol. 2011;719:415–33. doi:10.1007/978-1-61779-027-0_19.PubMedView ArticleGoogle Scholar
- Carrell DT, Aston KI, Oliva R, et al. The “omics” of human male infertility: integrating big data in a systems biology approach. Cell Tissue Res. 2016;363:295.PubMedView ArticleGoogle Scholar
- BIG Data Center Members. The BIG Data Center: from deposition to integration to translation. Nucleic Acids Res. 2017;45:D18–24.View ArticleGoogle Scholar
- Kim D, Joung JG, Sohn KA, et al. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc. 2015;22(1):109–20.PubMedGoogle Scholar
- Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68.PubMedPubMed CentralView ArticleGoogle Scholar
- Winterbach W, Mieghem P, Reinders M, et al. Topology of molecular interaction networks. BMC Syst Biol. 2013;7:90.PubMedPubMed CentralView ArticleGoogle Scholar
- Vogt H, Hofmann B, Getz L. The new holism: P4 systems medicine and the medicalization of health and life itself. Med Health Care Philos. 2016;19(2):307–23.PubMedPubMed CentralView ArticleGoogle Scholar
- Guo NL. Network medicine: New paradigm in the omics Era. Anat Physiol. 2011;1(1):1000e106.PubMedPubMed CentralGoogle Scholar
- Lecca P, Nguyen TP, Priami C, et al. Network inference from time-dependent omics data. Methods Mol Biol. 2011;719:435–55.PubMedView ArticleGoogle Scholar
- Machado D, Costa RS, Rocha M, et al. Modeling formalisms in systems biology. AMB Express. 2011;1:45.PubMedPubMed CentralView ArticleGoogle Scholar
- Liang Y, Kelemen A. Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments. J Funct Integr Genomics. 2006;6:1–13.View ArticleGoogle Scholar
- Liang, Y., Kelemen, A. (2016). Big Data Science and its Applications in Health and Medical Research: Challenges and Opportunities, Austin Journal of Biometrics & Biostatistics, 7(3). doi: 10.4172/2155-6180.1000307
- Kelemen, A., Liang, Y., Vasilakos, A. (2008). Review of Computational Intelligence for Gene-Gene Interactions in Disease Mapping, in “Computational Intelligence in Medical Informatics” (A. Kelemen, A. Abraham, Y. Chen, Eds.) in the Series in Studies in Computational Intelligence, 1-16Google Scholar
- Liu YY, Slotine JJ, Barabasi AL. Controllability of complex networks. Nature. 2011;473(7346):167–73.PubMedView ArticleGoogle Scholar
- Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One. 2010;5(2):e9202.PubMedPubMed CentralView ArticleGoogle Scholar
- Daniel M, Costello JC, Robert K, Nicole V, Prill RJ, Camacho DM, Allison KR. The DREAM5 Consortium, Manolis Kellis, James J Collins, & Gustavo Stolovitzky. Nature Methods. 2012;9(8):796–804.View ArticleGoogle Scholar
- Grigorov MG. Analysis of time course omics datasets. Methods Mol Biol. 2011;719:153–72.PubMedView ArticleGoogle Scholar
- Holter NS, Maritan A, Cieplak M, et al. Dynamic modeling of gene expression data. Proc Natl Acad Sci. 2001;98:1693–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Bar-Joseph Z, Gitter A, Simon I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet. 2012;13(8):552–64.PubMedView ArticleGoogle Scholar
- Bar-Joseph Z, Gerber GK, Gifford DK, et al. Continuous representations of time-series gene expression data. J Comput Biol. 2004;10(3-4):341–56.View ArticleGoogle Scholar
- Ramoni MF, Sebastiani P, Kohane IS. Cluster analysis of gene expression dynamics. Proc Natl Acad Sci. 2002;99(14):9121–6.PubMedPubMed CentralView ArticleGoogle Scholar
- de Jong H, Page M. Search for steady states of piecewise-linear differential equation models of genetic regulatory networks. IEEE/ACM Trans Comput Biol Bioinform. 2008;5(2):208–22.PubMedView ArticleGoogle Scholar
- Davidich M, Bornholdt S. The transition from differential equations to Boolean networks: a case study in simplifying a regulatory network model. J Theor Biol. 2008;255(3):269–77.PubMedView ArticleGoogle Scholar
- Le Novere N. Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet. 2015;16:146–58.PubMedPubMed CentralView ArticleGoogle Scholar
- Shmulevich, I., Dougherty, E. R. (2010). Probabilistic Boolean networks: The modeling and control of gene regulatory networks, SIAM Press.Google Scholar
- Mussel C, Hopfensitz M, Kestler HA. BoolNet-an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics. 2010;26(10):1378–80.PubMedView ArticleGoogle Scholar
- Monteiro PT, Ropers D, Mateescu R, et al. Temporal logic patterns for querying dynamic models of cellular interaction networks. Bioinformatics. 2008;24(16):i227–33.PubMedView ArticleGoogle Scholar
- Leek J, (2011) Asymptotic Conditional Singular Value Decomposition for High-Dimensional Genomic Data Biometrics. 67 (2), pp. 344–52.Google Scholar
- Carvalho CM, Chang J, Lucas JE, et al. High-dimensional sparse factor modelling: applications in gene expression genomics. J Am Stat Assoc. 2008;103(484):1438–56.PubMedPubMed CentralView ArticleGoogle Scholar
- Carvalho CM, West M. Dynamic matrix-variate graphical models. Bayesian Anal. 2007;2(1):69–97.View ArticleGoogle Scholar
- Carvalho CM, West M, Bernardo JM, et al. Dynamic matrix-variate graphical models—a synopsis. Bayesian statistics. VIII Oxford: Oxford University Press; 2007. p. 585–90.Google Scholar
- Peterson C, Stingo F, Vannucci M. Bayesian inference of multiple Gaussian graphical models. J Am Stat Assoc. 2014;110(509):159–74.PubMed CentralView ArticleGoogle Scholar
- Liang F, Song Q, Qiu P. An equivalent measure of partial correlation coefficients for high dimensional Gaussian graphical models. J Am Stat Assoc. 2015;110:1248.View ArticleGoogle Scholar
- Kossenkov AV, Ochs MF. Matrix factorization for recovery of biological processes from microarray data. Methods Enzymol. 2009;467:59–77.PubMedPubMed CentralView ArticleGoogle Scholar
- Ramsey S, Orrell D, Bolouri H. Dizzy: stochastic simulation of large-scale genetic regulatory networks. J Bioinform Comput Biol. 2005;3(2):415–36.PubMedView ArticleGoogle Scholar
- Chowdhury AR, Chetty M, Evans R. Stochastic S-system modeling of gene regulatory network. Cogn Neurodyn. 2015;9(5):535–47.PubMedPubMed CentralView ArticleGoogle Scholar
- Tanevski J, Todorovski L, Dzeroski S. Learning stochastic process-based models of dynamical systems from knowledge and data. BMC Syst Biol. 2016;22:10–30.Google Scholar
- Chen KC, Wang TY, Tseng HH, et al. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics. 2005;21(12):2883–90.PubMedView ArticleGoogle Scholar
- Swain MT, Mandel JJ, Dubitzky W. Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks. BMC Bioinform. 2010;11:459.View ArticleGoogle Scholar
- Rangel C, Angus J, Ghahramani Z, et al. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics. 2004;20(9):1361–72.PubMedView ArticleGoogle Scholar
- Yuan M, Kendziorski C. Hidden Markov models for microarray time course data in multiple biological conditions. J Am Stat Assoc. 2006;101(476):1323–32.View ArticleGoogle Scholar
- Perrin BE, Ralaivola L, Mazurie A, et al. Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003;19 Suppl 2:ii138–48.PubMedView ArticleGoogle Scholar
- Durbin J, Koopman SJ. Time series analysis for non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion), J. R Stat Soc, Series B. 2000;62:3–56.View ArticleGoogle Scholar
- Wolfinger RD, Gibson G, Wolfinger ED, et al. Assessing gene significance from cDNA microarray expression data via mixed models. J Comp Biol. 2001;8(6):625–37.View ArticleGoogle Scholar
- Fujita A, Sato JR, Garay-Malpartida HM, Yamaguchi R, Miyano S, Sogayar MC, Ferreira CE. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Syst Biol. 2007;1:39.PubMedPubMed CentralView ArticleGoogle Scholar
- Ernst J, Nau GJ, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics. 2005;21 suppl 1:i159–68.PubMedView ArticleGoogle Scholar
- de Hoon MJL, Imoto S, Miyano S. Statistical analysis of a small set of time-ordered gene expression data using linear splines. Bioinformatics. 2002;18(11):1477–85.PubMedView ArticleGoogle Scholar
- Coffey N, Hinde J. Analyzing time-course microarray data using functional data analysis - a review. Stat Appl Genet Mol Biol. 2011;10:1544–6115.View ArticleGoogle Scholar
- Mitra R, Müller P, Liang S, et al. A Bayesian graphical model for chip-seq data on histone modifications. J Am Stat Assoc. 2013;108:69–90.View ArticleGoogle Scholar
- Ferrazzi F, Sebastiani P, Ramoni MF, et al. Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks. BMC Bioinform. 2007;8 Suppl 5:S2. doi:10.1186/1471-2105-8-S5-S2.View ArticleGoogle Scholar
- Troyanskaya OG, Dolinski K, Owen AB, et al. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci U S A. 2003;100(14):8348–53.PubMedPubMed CentralView ArticleGoogle Scholar
- Liang Y, Kelemen A. Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns. Biom J. 2009;51(1):56–69.PubMedView ArticleGoogle Scholar
- Liang Y, Kelemen A. Bayesian models and meta analysis for multiple tissue gene expression data following corticosteriod administration. BMC Bioinform. 2008;9:354.View ArticleGoogle Scholar
- Liang Y, Kelemen A. Bayesian state space models for inferring and predicting temporal gene expression profiles. Biom J. 2007;49(6):801–14.PubMedView ArticleGoogle Scholar
- Liang Y, Kelemen A. Bayesian state space models for dynamic genetic network construction across multiple tissues. J Stat Appl Genet Mol Biol. 2016;15(4):273–90.Google Scholar
- Marinazzo D, Pellicoro M, Stramaglia S. Kernel-Granger causality and the analysis of dynamical networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2008;77(5 Pt 2):056215.PubMedView ArticleGoogle Scholar
- Gasch, A. P., Eisen, M. B. (2002). Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3(11).Google Scholar
- Huang, H., Cai, L., Wong, W. H. (2008). Clustering analysis of SAGE transcription profiles using a Poisson approach. in SAGE: Methods and Protocols, ed. K. L. Nielsen, Humana Press Inc.Google Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998;95(25):14863–8.PubMedPubMed CentralView ArticleGoogle Scholar
- D’haeseleer P. How does gene expression clustering work? Nat Biotechnol. 2005;23:1499–501.PubMedView ArticleGoogle Scholar
- Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics. 2001;17(9):763–74.PubMedView ArticleGoogle Scholar
- Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999;6:2907–12.View ArticleGoogle Scholar
- Fowler A, Menon V, Heard NA. Dynamic Bayesian clustering. J Bioinform Comput Biol. 2013;11(5):1342001.PubMedView ArticleGoogle Scholar
- D’haeseleer P, Liang S, Somogyi R. Genetic network inference: from co expression clustering to reverse engineering. Bioinformatics. 2000;16:707–26.PubMedView ArticleGoogle Scholar
- Dettleing, M. and Bühlmann, P. (2002). Supervised clustering of genes. Genome Biology. 3:research0069.1-0069.15.Google Scholar
- Zhang Y, Tibshirani R, Davis R. Classification of patients from time-course gene expression. Biostatistics. 2013;14(1):87–98.PubMedView ArticleGoogle Scholar
- Komura D, Nakamura H, Tsutsumi S, et al. Multidimensional support vector machines for visualization of gene expression data. Bioinformatics. 2005;21(4):439–44.PubMedView ArticleGoogle Scholar
- Liang Y, Kelemen A. Time lagged recurrent neural network for temporal gene expression classification. Int J Comput Intell Bioinform Syst Biol. 2009;1(1):91–102.Google Scholar
- Liang Y, Kelemen A. Temporal gene expression classification with regularised neural network. Int J Bioinform Res Appl. 2005;1(4):399–413.PubMedView ArticleGoogle Scholar
- Xu R, Wang Q. A semi-supervised pattern-learning approach to extract Pharmacogenomics-specific drug-gene pairs from biomedical literature. J Pharmacogenom Pharmacoproteomics. 2013;4:117.Google Scholar
- Shi M, Zhang B. Semi-supervised learning improves gene expression-based prediction of cancer recurrence. Bioinformatics. 2011;27(21):3017–23.PubMedPubMed CentralView ArticleGoogle Scholar
- Kelemen A, Zhou H, Lawhead P, et al. Naive Bayesian classifier for microarray data. IEEE Proc Int Jt Conf Neural Netw. 2003;3:1769–73.Google Scholar
- Liang Y, Kelemen A. Hierarchical Bayesian neural network for gene expression temporal patterns. J Stat Appl Genet Mol Biol. 2004;3(1):1–23.View ArticleGoogle Scholar
- Peng H-K, Marculescu R. Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning. PLoS One. 2015;10(4):e0118309.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004;20(18):3594–603.PubMedView ArticleGoogle Scholar
- Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol. 2008;9(10):770–80.PubMedView ArticleGoogle Scholar
- Marbach D, Costello JC, Küffner R, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9(8):796–804.PubMedPubMed CentralView ArticleGoogle Scholar
- Saris CGJ, Horvath S, van Vught PWJ, et al. Weighted gene co-expression network analysis of the peripheral blood from amyotrophic lateral sclerosis patients. BMC Genomics. 2009;10(1):405.PubMedPubMed CentralView ArticleGoogle Scholar
- Ghasemi O, Lindsey ML, Yang T, et al. Bayesian parameter estimation for nonlinear modeling of biological pathways. BMC Syst Biol. 2011;5 Suppl 3:S9.PubMedPubMed CentralView ArticleGoogle Scholar
- Boué, S., Talikka, M., Westra, J. W., et al. (2015). Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database. Article ID bav030.Google Scholar
- Cerami E, Demir E, Schultz N, et al. Automated network analysis identifies core pathways in glioblastoma. PLoS One. 2010;5(2):e8918.PubMedPubMed CentralView ArticleGoogle Scholar
- Jang Y, Yu N, Seo J, et al. MONGKIE: an integrated tool for network analysis and visualization for multi-omics data. Biol Direct. 2016;11:10.PubMedPubMed CentralView ArticleGoogle Scholar
- Hecker M, Lambeck S, Toepfer S, et al. Gene regulatory network inference: data integration in dynamic models-a review. Biosystems. 2009;96(1):86–103.PubMedView ArticleGoogle Scholar
- Junker BH, Klukas C, Schreiber F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinforma. 2006;7:109. doi:10.1186/1471-2105-7-109.View ArticleGoogle Scholar
- Noren DP, Long BL, Norel R, Rrhissorrakrai K, Hess K, et al. A Crowdsourcing approach to developing and assessing prediction algorithms for AML prognosis. PLoS Comput Biol. 2016;12(6):e1004890.PubMedPubMed CentralView ArticleGoogle Scholar
- Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMedPubMed CentralView ArticleGoogle Scholar
- Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe’er D. Wishbone identifies bifurcating developmental trajectories from single-cell data Nat. Biotech. 2016;34:637–45.Google Scholar
- Litvin O, Causton H, Chen BJ, Pe’er D. Modularity and interactions in the genetics of gene expression. Proc Natl Acad Sci. 2009;106:6441–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Marbach D, Schaffter T, Mattiussi C, et al. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16(2):229–39.PubMedView ArticleGoogle Scholar
- Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A. 2010;107(14):6286–91.PubMedPubMed CentralView ArticleGoogle Scholar
- Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating realistic “in silico” gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16(2):229–39.PubMedView ArticleGoogle Scholar
- Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003;34(2):166–76.PubMedView ArticleGoogle Scholar
- Pal R, Bhattacharya S. Transient dynamics of reduced-order models of genetic regulatory networks. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(4):1230–44.PubMedView ArticleGoogle Scholar
- Wang YK, Hurley DG, Schnell S, et al. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks. PLoS One. 2013;8(8):e72103.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang J, Chen G, Li M, et al. Integration of breast cancer gene signature based on graph centrality. BMC Syst Biol. 2011;5 Suppl 3:S10.PubMedPubMed CentralView ArticleGoogle Scholar
- Foster DV, Kauffman SA, Socolar JES. Network growth models and genetic regulatory networks. Phys Rev E. 2006;73:031912.View ArticleGoogle Scholar
- Yu H, Kim PM, Sprecher E, et al. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3(4):e59.PubMedPubMed CentralView ArticleGoogle Scholar
- Ideker, T., Krogan, N. J. (2012). Differential network biology. Mol Syst Biol. 8(565). doi: 10.1038/msb.2011.99
- Bhardwaj N, Kim PM, Gerstein MB. Rewiring of transcriptional regulatory networks: Hierarchy, rather than connectivity, better reflects the importance of regulators. Sci Signal. 2010;3(146):ra79. doi:10.1126/scisignal.2001014.PubMedView ArticleGoogle Scholar
- Kourmpetis YAI, van Dijk ADJ, Bink MCAM, et al. Bayesian Markov random field analysis for protein function prediction based on network data. PLoS One. 2010;5(2):e9293.PubMedPubMed CentralView ArticleGoogle Scholar
- Yao C, Li H, Zhou C, et al. Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol. 2010;4:151.PubMedPubMed CentralView ArticleGoogle Scholar
- Sophie Lèbre, Jennifer Becq, Frédéric Devaux, Michael PH Stumpf, Gaëlle Lelandais (2010) Statistical inference of the time-varying structure of gene-regulation networks BMC Systems Biology,,4 (1)Google Scholar
- Carter SL, Brechbühler CM, Griffin M, et al. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242–50.PubMedView ArticleGoogle Scholar
- Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.View ArticleGoogle Scholar
- Dondelinger F, Lèbre S, Husmeier D. Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach Learn. 2013;90:191.View ArticleGoogle Scholar
- Dojer N, Gambin A, Mizera A, et al. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinform. 2006;7:249.View ArticleGoogle Scholar
- Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2005;21(1):71–9.PubMedView ArticleGoogle Scholar
- Li P, Zhang CY, Perkins EJ, et al. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinform. 2007;8 Suppl 7:S13. doi:10.1186/1471-2105-8-S7-S13.View ArticleGoogle Scholar
- Grzegorczyk M, Husmeier D. Non-homogeneous dynamic Bayesian networks for continuous data. Mach Learn. 2011;83:355.View ArticleGoogle Scholar
- Wilkinson DJ. Stochastic modelling for systems biology. 2nd ed. New York: CRC Press; 2011.Google Scholar
- Whiteley N, Andrieu C, Doucet A. Efficient Bayesian inference for switching state-space models using discrete particle Markov chain Monte Carlo methods. ArXiv e-prints. 2010;1011:2437.Google Scholar
- Friedman N, Inferring cellular networks using probabilistic graphical models Carvalho, C. M., West, M. Dynamic matrix-variate graphical models. Bayesian Anal. 2007;2(1):69–97.View ArticleGoogle Scholar
- Zou C, Feng H. Granger causality vs. Dynamic Bayesian network inference: a comparative study. BMC Bioinform. 2009;10:122.View ArticleGoogle Scholar
- Kimm SY, Imoto S, Miyano S. Dynamic Bayesian network and nonparametric regression model for inferring gene networks. Genome Inform. 2002;13:371–2.Google Scholar
- Robinson J, Hartemink A. Learning Non-stationary dynamic Bayesian networks. J Mach Learn Res. 2010;11:3647–80.Google Scholar
- Michailidis G, d‘Alché-Buc F. Autoregressive models for gene regulatory network inference: sparsity, stability and causality. Math Biosci. 2013;246(2):326–34.PubMedView ArticleGoogle Scholar
- Furqan MS, Siyal MY. Elastic-Net copula granger causality for inference of biological networks. PLoS One. 2016;11(10):e0165612.PubMedPubMed CentralView ArticleGoogle Scholar
- Furqan MS, Siyal MY. Inference of biological networks using Bi-directional random forest granger causality. Springerplus. 2016;5:514.PubMedPubMed CentralView ArticleGoogle Scholar
- Tam GH, Chang C, Hung YS. Gene regulatory network discovery using pairwise granger causality. ET Syst Biol. 2013;7(5):195–204.Google Scholar
- Yao S, Yoo S, Yu D. Prior knowledge driven granger causality analysis on gene regulatory network discovery. BMC Bioinform. 2015;16:273.View ArticleGoogle Scholar
- Lozano AC, Abe N, Liu Y, Rosset S. Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics. 2009;25(12):i110–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Acerbi E, Zelante T, Narang V, Stella F. Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation. BMC Bioinform. 2014;15(387):1471–2105.Google Scholar
- Kandasamy K, Mohan SS, Raju R, et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 2010;11:R3.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu N, Seo J, Rho K, et al. hiPathDB: a human-integrated pathway database with facile visualization. Nucleic Acids Res. 2012;40(Database issue):D797–802.PubMedView ArticleGoogle Scholar
- Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list. Nucleic Acids Res. 2006;34:D504–506. doi:10.1093/nar/gkj126.PubMedView ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009;4(1):44–57.View ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13.View ArticleGoogle Scholar
- Kilicoglu H, Shin D, Fiszman M, et al. SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics. 2012;28(23):3158–60.PubMedPubMed CentralView ArticleGoogle Scholar
- Hu Z, Hung JH, Wang Y, Chang YC, Huang CL, Huyck M, DeLisi C. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 2009;37:W115–121. doi:10.1093/nar/gkp406.PubMedPubMed CentralView ArticleGoogle Scholar
- Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ, The HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G+, Saez-Rodriguez J+ & Mukherjee S+. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13:310–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Costello J, Heiser L, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32:1202–12.PubMedPubMed CentralView ArticleGoogle Scholar
- Cambiaghi, A., Ferrario, M., Masseroli, M. (2016). Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics pii: bbw031.Google Scholar
- Lei L, Tibiche C, Fu C, et al. The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer. Genome Res. 2012;22(7):1222–30.View ArticleGoogle Scholar
- Gut G, Tadmor MD, Pe’er D, Pelkmans L, Liberali P. Trajectories of cell-cycle progression from fixed cell populations. Nat Methods. 2015;12(10):951–4.PubMedView ArticleGoogle Scholar
- Gagneur J, Stegle O, Zhu C, Jakob P, Tekkedil MM, Aiyar RS, Schuon AK, Pe’er D, Steinmetz LM. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet. 2013;9(9):e1003803.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang J, Qiu X, Deng Y, et al. A transcriptional dynamic network during Arabidopsis thaliana pollen development. BMC Syst Biol. 2011;5 Suppl 3:S8.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia P, Kao CF, Kuo PH, et al. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol. 2011;5 Suppl 3:S12. doi:10.1186/1752-0509-5-S3-S12.PubMedPubMed CentralView ArticleGoogle Scholar
- Xie L, Weichel B, Ohm JE, et al. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5 Suppl 3:S4.PubMedPubMed CentralView ArticleGoogle Scholar
- Kim W, Li M, Wang J, et al. Biological network motif detection and evaluation. BMC Syst Biol. 2011;5 Suppl 3:S5.PubMedPubMed CentralView ArticleGoogle Scholar
- Martin G, Marinescu MC, Singh DE, et al. Leveraging social networks for understanding the evolution of epidemics. BMC Syst Biol. 2011;5 Suppl 3:S14.PubMedPubMed CentralView ArticleGoogle Scholar
- Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A. Paintomics a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137–9. doi:10.1093/bioinformatics/btq594.PubMedView ArticleGoogle Scholar
- Durruthy, R & Heller, S (2015). Applications for single cell trajectory analysis in inner ear development and regeneration. Cell and Tissue Research, 361(1), 49–7. http://doi.org/10.1007/s00441-014-2079-2.
- Simidjievski N, Todorovski L, Dzeroski S. Modeling dynamic systems with efficient ensembles of process-based models. PLoS One. 2016;11(4):e0153507.PubMedPubMed CentralView ArticleGoogle Scholar
- Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137–9. doi:10.1093/bioinformatics/btq594.PubMedView ArticleGoogle Scholar
- Trauger SA, Kalisak E, Kalisiak J, Morita H, Weinberg MV, Menon AL, Ii Poole FL, Adams MWW, Siuzdak G. Correlating the transcriptome, proteome, and Metabolome in the environmental adaptation of a Hyperthermophile. J Proteome Res. 2008;7:1027–35. doi:10.1021/pr700609j.PubMedView ArticleGoogle Scholar
- Sperisen P, Cominetti O, Martin F-PJ. Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research. Front Mol Biosci. 2015;2:44. http://doi.org/10.3389/fmolb.2015.00044.PubMedPubMed CentralView ArticleGoogle Scholar
- Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, Sartor MA, Stringer KA, Jagadish HV, Burant C, et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics. 2012;28:373–80. doi:10.1093/bioinformatics/btr661.PubMedView ArticleGoogle Scholar
- Pavlopoulos G, O’Donoghue S, Satagopam V, Soldatos T, Pafilis E, Schneider R. Arena3D: visualization of biological networks in 3D. BMC Syst Biol. 2008;2:104. doi:10.1186/1752-0509-2-104.PubMedPubMed CentralView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. doi:10.1101/gr.1239303.PubMedPubMed CentralView ArticleGoogle Scholar
- McGuffin MJ, Jurisica I. Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans Vis Comput Graph. 2009;15:937–44.PubMedView ArticleGoogle Scholar
- Barsky A, Munzner T, Gardy J, Kincaid R: C. Visualizing multiple experimental conditions on a graph with biological context. IEEE Trans Vis Comput Graph. 2008;14:1253–60.PubMedView ArticleGoogle Scholar
- Yordanov, B., Dunn, S. J., Kugler, H., et al. (2016). A method to identify and analyze biological programs through automated reasoning. NP J Systems Biology and Applications. Article number: 16010.Google Scholar
- Fertig EJ, Stein-O’Brien G, Jaffe A, et al. Pattern identification in time-course gene expression data with the CoGAPS matrix factorization. Methods Mol Biol. 2014;1101:87–112.PubMedView ArticleGoogle Scholar
- Fertig EJ, Ding J, Favorov AV, et al. CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data. Bioinformatics. 2010;26(21):2792–3.PubMedPubMed CentralView ArticleGoogle Scholar
- Vrahatis AG, Dimitrakopoulou K, Balomenos P, Tsakalidis AK, Bezerianos A. CHRONOS: a time-varying method for microRNA-mediated sub-pathway enrichment analysis. Bioinformatics. 2015;32(6):884–92.PubMedView ArticleGoogle Scholar
- Kanehisa M, Goto S, Sato Y, et al. KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 2012;40:D109–14. doi:10.1093/nar/gkr988.PubMedView ArticleGoogle Scholar
- de Pretis S, Kress T, Morelli MJ, Melloni GE, Rival L, Amati B, Pelizzola M. INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments. Bioinformatics. 2015;31(17):2829–35.PubMedView ArticleGoogle Scholar
- Batt G, Besson B, Ciron PE, et al. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. In: van Helden J, Toussaint A, Thieffry D, editors. Bacterial molecular networks : methods and protocols, methods in molecular biology. New York: Humana Press, Springer; 2012. p. 439–62.View ArticleGoogle Scholar
- Dojer N, Bednarz P, Podsiadło A, et al. BNFinder2: faster Bayesian network learning and Bayesian classification. Bioinformatics. 2013;29(16):2068–70.PubMedPubMed CentralView ArticleGoogle Scholar
- Wilczynski B, Dojer N. BNFinder: exact and efficient method for learning Bayesian networks. Bioinformatics. 2009;25(2):286–7.PubMedView ArticleGoogle Scholar
- Villa S, Stella F. Learning continuous time Bayesian networks in Non-stationary domains. J Artif Intel Res. 2016;57:1–37.Google Scholar