Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.     

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other’s state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.     

Node-based differential network analysis in genomics

Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.     

Using gene expression programming to infer gene regulatory networks from time-series data

Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.     

Inference of gene regulatory networks with multi-objective cellular genetic algorithm

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

Characterizing mutation–expression network relationships in multiple cancers

BackgroundData made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics.ResultsUsing somatic mutation and gene expression information across 19 cancers, we generated mutation–expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p < 0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load.ConclusionsThis study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation–expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.     

Hierarchical closeness efficiently predicts disease genes in a directed signaling network

BackgroundMany structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks.ResultsIn this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes.ConclusionTaken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.     

基因网络前馈环路中的涨落共振

应用化学主方程和线性涨落近似方法,重点研究了前馈环路（FFL）对外界输入弱信号的响应,特别考察了它的涨落共振现象．研究发现Z基因的FR行为很大程度上依赖于FFL的协同性：协同FFL中Z的FR曲线呈明显的单峰,而非协同FFL中该曲线出现明显双峰．由于振荡信号常常在实际应用中用来探测网络的调控结构,因此可以利用涨落共振曲线的定性差别来区分FFL网络的性能．     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Ordinary differential equations (ODE) have been widely used for modeling and analysis of dynamic gene networks in systems biology. In this paper, we propose an optimization method that can infer a gene regulatory network from time-series gene expression data. Specifically, the following four cases are considered: (1) reconstruction of a gene network from synthetic gene expression data with noise, (2) reconstruction of a gene network from synthetic gene expression data with time-delay, (3) reconstruction of a gene network from synthetic gene expression data with noise and time-delay, and (4) reconstruction of a gene network from experimental time-series data in budding yeast cell cycle.     

Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization

BackgroundFinding candidate genes associated with a disease is an important issue in biomedical research. Recently, many network-based methods have been proposed that implicitly utilize the modularity principle, which states that genes causing the same or similar diseases tend to form physical or functional modules in gene/protein relationship networks. Of these methods, the random walk with restart (RWR) algorithm is considered to be a state-of-the-art approach, but the modularity principle has not been fully considered in traditional RWR approaches. Therefore, we propose a novel method called ORIENT (neighbor-favoring weight reinforcement) to improve the performance of RWR through proper intensification of the weights of interactions close to the known disease genes.ResultsThrough extensive simulations over hundreds of diseases, we observed that our approach performs better than the traditional RWR algorithm. In particular, our method worked best when the weights of interactions involving only the nearest neighbor genes of the disease genes were intensified. Interestingly, the performance of our approach was negatively related to the probability with which the random walk will restart, whereas the performance of RWR without the weight-reinforcement was positively related in dense gene/protein relationship networks. We further found that the density of the disease gene-projected sub-graph and the number of paths between the disease genes in a gene/protein relationship network may be explanatory variables for the RWR performance. Finally, a comparison with other well-known gene prioritization tools including Endeavour, ToppGene, and BioGraph, revealed that our approach shows significantly better performance.ConclusionTaken together, these findings provide insight to efficiently guide RWR in disease gene prioritization.