DREAM4 Challenge Research Articles

Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs.

Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)-target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

GReNaDIne: A Data-Driven Python Library to Infer Gene Regulatory Networks from Gene Expression Data.

Context: Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one. This step can be particularly difficult and time consuming, since most methods' implementations are made available independently, possibly in different programming languages. The implementation of an open-source library containing different inference methods within a common framework is expected to be a valuable toolkit for the systems biology community. Results: In this work, we introduce GReNaDIne (Gene Regulatory Network Data-driven Inference), a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes eight generalist preprocessing techniques, suitable for both RNA-seq and microarray dataset analysis, as well as four normalization techniques dedicated to RNA-seq. In addition, this package implements the possibility to combine the results of different inference tools to form robust and efficient ensembles. This package has been successfully assessed under the DREAM5 challenge benchmark dataset. The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. The latest documentation on the GReNaDIne library is also available at Read the Docs, an open-source software documentation hosting platform. Contribution: The GReNaDIne tool represents a technological contribution to the field of systems biology. This package can be used to infer gene regulatory networks from high-throughput gene expression data using different algorithms within the same framework. In order to analyze their datasets, users can apply a battery of preprocessing and postprocessing tools and choose the most adapted inference method from the GReNaDIne library and even combine the output of different methods to obtain more robust results. The results format provided by GReNaDIne is compatible with well-known complementary refinement tools such as PYSCENIC.

AGRN: accurate gene regulatory network inference using ensemble machine learning methods.

Biological processes are regulated by underlying genes and their interactions that form gene regulatory networks (GRNs). Dysregulation of these GRNs can cause complex diseases such as cancer, Alzheimer's and diabetes. Hence, accurate GRN inference is critical for elucidating gene function, allowing for the faster identification and prioritization of candidate genes for functional investigation. Several statistical and machine learning-based methods have been developed to infer GRNs based on biological and synthetic datasets. Here, we developed a method named AGRN that infers GRNs by employing an ensemble of machine learning algorithms. From the idea that a single method may not perform well on all datasets, we calculate the gene importance scores using three machine learning methods-random forest, extra tree and support vector regressors. We calculate the importance scores from Shapley Additive Explanations, a recently published method to explain machine learning models. We have found that the importance scores from Shapley values perform better than the traditional importance scoring methods based on almost all the benchmark datasets. We have analyzed the performance of AGRN using the datasets from the DREAM4 and DREAM5 challenges for GRN inference. The proposed method, AGRN-an ensemble machine learning method with Shapley values, outperforms the existing methods both in the DREAM4 and DREAM5 datasets. With improved accuracy, we believe that AGRN inferred GRNs would enhance our mechanistic understanding of biological processes in health and disease. https://github.com/DuaaAlawad/AGRN. Supplementary data are available at Bioinformatics online.

Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.

Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.

Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks

Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem. Gene expression datasets consist of thousand of genes with relatively small sample sizes (i.e. are large-p-small-n). Moreover, dependencies of various orders coexist in the datasets. On the one hand transcription factor encoding genes act like hubs and regulate target genes, on the other hand target genes show local dependencies. In the field of Undirected Network Models (UNMs)—a subclass of PNMs—the Glasso algorithm has been proposed to deal with high dimensional microarray datasets forcing sparsity. To overcome the problem of the complex structure of interactions, modifications of the default Glasso algorithm have been developed that integrate the expected dependency structure in the UNMs beforehand. In this work we advocate the use of a simple score-based Hill Climbing algorithm (HC) that learns Gaussian Bayesian networks leaning on directed acyclic graphs. We compare HC with Glasso and variants in the UNM framework based on their capability to reconstruct GRNs from microarray data from the benchmarking synthetic dataset from the DREAM5 challenge and from real-world data from the Escherichia coli genome. We conclude that dependencies in complex data are learned best by the HC algorithm, presenting them most accurately and efficiently, simultaneously modelling strong local and weaker but significant global connections coexisting in the gene expression dataset. The HC algorithm adapts intrinsically to the complex dependency structure of the dataset, without forcing a specific structure in advance.

Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases.

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼ 8800 protein kinase/phosphatase-transcription factor interactions and ∼ 6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.

PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.

The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process.

ComHub: Community predictions of hubs in gene regulatory networks

BackgroundHub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.ResultsWe have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.ConclusionsIn contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

An Ensemble Method to Reconstruct Gene Regulatory Networks Based on Multivariate Adaptive Regression Splines.

Gene regulatory networks (GRNs) play a key role in biological processes. However, GRNs are diverse under different biological conditions. Reconstructing gene regulatory networks (GRNs) from gene expression has become an important opportunity and challenge in the past decades. Although there are a lot of existing methods to infer the topology of GRNs, such as mutual information, random forest, and partial least squares, the accuracy is still low due to the noise and high dimension of the expression data. In this paper, we introduce an ensemble Multivariate Adaptive Regression Splines (MARS) based method to reconstruct the directed GRNs from multifactorial gene expression data, called PBMarsNet. PBMarsNet incorporates part mutual information (PMI) to pre-weight the candidate regulatory genes and then uses MARS to detect the nonlinear regulatory links. Moreover, we apply bootstrap to run the MARS multiple times and average the outputs of each MARS as the final score of regulatory links. The results on DREAM4 challenge and DREAM5 challenge datasets show PBMarsNet has a superior performance and generalization over other state-of-the-art methods.

Inferring of regulatory networks from expression data using Bayesian networks

Subject of Research.The paper considers the inferring of gene regulatory networks in the form of Bayesian networks from gene expression data. We present this problem as the problem of the marginal probability estimation for each edge appearance in the true Bayesian network under the known gene expression levels. Monte Carlo approach based on the Markov chains is proposed. Method. The proposed method involved the sampling of Bayesian network pairs and a discretization policy, providing a way for the network to be applied to continuous gene expression dataaccording to a posteriori distribution. The Markov chain Monte Carlo approach was used for sampling with implementation via the Metropolis-Hastings algorithm. Then, the desired probabilities were estimated based on the obtained sample. Main Results. The proposed method is tested on simulated data from the DREAM4 Challenges. Comparison with the leaders shows that the developed method quality surpasses the leader among the existing methods, the regularized gradient boosting machines method (RGBM), on some tests and is comparable on the othersin view of the results. At the same time, the proposed method is flexible enough and can be adapted to the other types of experimental data. Practical Relevance. The method is applicable in computational biology for research of the gene regulation mechanisms in various processes, including the tumor growth or the immune system operation.

Ensembles of extremely randomized predictive clustering trees for predicting structured outputs

We address the task of learning ensembles of predictive models for structured output prediction (SOP). We focus on three SOP tasks: multi-target regression (MTR), multi-label classification (MLC) and hierarchical multi-label classification (HMC). In contrast to standard classification and regression, where the output is a single (discrete or continuous) variable, in SOP the output is a data structure—a tuple of continuous variables MTR, a tuple of binary variables MLC or a tuple of binary variables with hierarchical dependencies (HMC). SOP is gaining increasing interest in the research community due to its applicability in a variety of practically relevant domains. In this context, we consider the Extra-Tree ensemble learning method—the overall top performer in the DREAM4 and DREAM5 challenges for gene network reconstruction. We extend this method for SOP tasks and call the extension Extra-PCTs ensembles. As base predictive models we propose using predictive clustering trees (PCTs)–a generalization of decision trees for predicting structured outputs. We conduct a comprehensive experimental evaluation of the proposed method on a collection of 41 benchmark datasets: 21 for MTR, 10 for MLC and 10 for HMC. We first investigate the influence of the size of the ensemble and the size of the feature subset considered at each node. We then compare the performance of Extra-PCTs to other ensemble methods (random forests and bagging), as well as to single PCTs. The experimental evaluation reveals that the Extra-PCTs achieve optimal performance in terms of predictive power and computational cost, with 50 base predictive models across the three tasks. The recommended values for feature subset sizes vary across the tasks, and also depend on whether the dataset contains only binary and/or sparse attributes. The Extra-PCTs give better predictive performance than a single tree (the differences are typically statistically significant). Moreover, the Extra-PCTs are the best performing ensemble method (except for the MLC task, where performances are similar to those of random forests), and Extra-PCTs can be used to learn good feature rankings for all of the tasks considered here.

GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem.

Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements. This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets. All of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA. Supplementary data are available at Bioinformatics online.

A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks.

Bayesian networks is a powerful method for identifying causal relationships among variables. However, as the network size increases, the time complexity of searching the optimal structure grows exponentially. We proposed a novel search algorithm - Fast and Furious Bayesian Network (FFBN). Compared to the existing greedy search algorithm, FFBN uses significantly fewer model configuration rules to determine the causal direction of edges when constructing the Bayesian network, which leads to greatly improved computational speed. We benchmarked the performance of FFBN by reconstructing gene regulatory networks (GRNs) from two DREAM5 challenge datasets: a synthetic dataset and a larger yeast transcriptome dataset. In both datasets, FFBN shows a much faster speed than the existing greedy search algorithm, while maintaining equally good or better performance in recall and precision. We then constructed three whole transcriptome GRNs for primary liver cancer (PL), primary colon cancer (PC) and colon to liver metastasis (CLM) expression data, which the existing greedy search algorithms failed. Three GRNs contain 12,099 common genes. Unprecedentedly, our newly developed FFBN algorithm is able to build up GRNs at a scale larger than 10,000 genes. Using FFBN, we discovered that CLM has its unique cancer molecular mechanisms and shares a certain degree of similarity with both PL and PC.

Robust network inference using response logic.

MotivationA major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context.ResultsWe present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system’s components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4 challenges. Applied to a novel perturbation dataset on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities toward various targeted inhibitors due to different PI3K mutants.Availability and implementationA Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Data and analysis scripts are available at github.com/GrossTor/response-logic-projects.Supplementary information Supplementary data are available at Bioinformatics online.

Ensemble multi‐objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps

Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEA FCM -GRN, to reconstruct GRNs based on FCMs. In EMOEA FCM -GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEA FCM -GRN is efficient and able to reconstruct GRNs accurately.

MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data

BackgroundReconstruction of gene regulatory networks (GRNs), also known as reverse engineering of GRNs, aims to infer the potential regulation relationships between genes. With the development of biotechnology, such as gene chip microarray and RNA-sequencing, the high-throughput data generated provide us with more opportunities to infer the gene-gene interaction relationships using gene expression data and hence understand the underlying mechanism of biological processes. Gene regulatory networks are known to exhibit a multiplicity of interaction mechanisms which include functional and non-functional, and linear and non-linear relationships. Meanwhile, the regulatory interactions between genes and gene products are not spontaneous since various processes involved in producing fully functional and measurable concentrations of transcriptional factors/proteins lead to a delay in gene regulation. Many different approaches for reconstructing GRNs have been proposed, but the existing GRN inference approaches such as probabilistic Boolean networks and dynamic Bayesian networks have various limitations and relatively low accuracy. Inferring GRNs from time series microarray data or RNA-sequencing data remains a very challenging inverse problem due to its nonlinearity, high dimensionality, sparse and noisy data, and significant computational cost, which motivates us to develop more effective inference methods.ResultsWe developed a novel algorithm, MICRAT (Maximal Information coefficient with Conditional Relative Average entropy and Time-series mutual information), for inferring GRNs from time series gene expression data. Maximal information coefficient (MIC) is an effective measure of dependence for two-variable relationships. It captures a wide range of associations, both functional and non-functional, and thus has good performance on measuring the dependence between two genes. Our approach mainly includes two procedures. Firstly, it employs maximal information coefficient for constructing an undirected graph to represent the underlying relationships between genes. Secondly, it directs the edges in the undirected graph for inferring regulators and their targets. In this procedure, the conditional relative average entropies of each pair of nodes (or genes) are employed to indicate the directions of edges. Since the time delay might exist in the expression of regulators and target genes, time series mutual information is combined to cooperatively direct the edges for inferring the potential regulators and their targets. We evaluated the performance of MICRAT by applying it to synthetic datasets as well as real gene expression data and compare with other GRN inference methods. We inferred five 10-gene and five 100-gene networks from the DREAM4 challenge that were generated using the gene expression simulator GeneNetWeaver (GNW). MICRAT was also used to reconstruct GRNs on real gene expression data including part of the DNA-damaged response pathway (SOS DNA repair network) and experimental dataset in E. Coli. The results showed that MICRAT significantly improved the inference accuracy, compared to other inference methods, such as TDBN, etc.ConclusionIn this work, a novel algorithm, MICRAT, for inferring GRNs from time series gene expression data was proposed by taking into account dependence and time delay of expressions of a regulator and its target genes. This approach employed maximal information coefficients for reconstructing an undirected graph to represent the underlying relationships between genes. The edges were directed by combining conditional relative average entropy with time course mutual information of pairs of genes. The proposed algorithm was evaluated on the benchmark GRNs provided by the DREAM4 challenge and part of the real SOS DNA repair network in E. Coli. The experimental study showed that our approach was comparable to other methods on 10-gene datasets and outperformed other methods on 100-gene datasets in GRN inference from time series datasets.

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.

Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impact of these variations are proposed in the literature, the results obtained are mostly in disagreement, entailing a considerable number of false-positive predictions. For this reason, we propose an approach based on Logistic Model Trees that integrates the predictions of different eQTL mapping tools to produce more reliable results. More precisely, we employ a machine learning-based method using logistic functions to perform a linear regression able to classify the predictions of three eQTL analysis tools (namely, R/qtl, MatrixEQTL, and mRMR). Given the lack of a reference dataset and that computational predictions are not so easy to test experimentally, the performance of our approach is assessed using data from the DREAM5 challenge. The results show the quality of the aggregated prediction is better than that obtained by each single tool in terms of both precision and recall. We also performed a test on real data, employing genotypes and microRNA expression profiles from Caenorhabditis elegans, which proved that we were able to correctly classify all the experimentally validated eQTLs. These good results come both from the integration of the different predictions, and from the ability of this machine learning algorithm to find the best cutoff thresholds for each tool. This combination makes our integration approach suitable for improving eQTL predictions for testing in a laboratory, reducing the number of false-positive results.

An approach for reduction of false predictions in reverse engineering of gene regulatory networks

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives.

Inferring large graphs using ell _1-penalized likelihood

We address the issue of recovering the structure of large sparse directed acyclic graphs from noisy observations of the system. We propose a novel procedure based on a specific formulation of the ell _1-norm regularized maximum likelihood, which decomposes the graph estimation into two optimization sub-problems: topological structure and node order learning. We provide convergence inequalities for the graph estimator, as well as an algorithm to solve the induced optimization problem, in the form of a convex program embedded in a genetic algorithm. We apply our method to various data sets (including data from the DREAM4 challenge) and show that it compares favorably to state-of-the-art methods. This algorithm is available on CRAN as the R package GADAG.