Pattern Mining Across Many Massive Biological Networks

Data integration and analysis of biological networks

Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level, and toward intelligent, efficient, and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein abundance, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is an exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in a public database, along with the confidence held in each edge (if available), can be fed into CABIN as one "evidence network," using the Cytoscape SIF file format. Using CABIN, one may increase the confidence in individual edges in a network inferred by an algorithm in SEBINI, as well as extend such a network by combining it with species-specific or generic information, e.g., known protein-protein interactions or target genes identified for known transcription factors. Thus, the combined SEBINI-CABIN toolkit aids in the more accurate reconstruction of biological networks, with less effort, in less time.A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/SEBINI/RootServlet . Source code and PostgreSQL database schema are available under open source license. ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. CABIN can be downloaded from http://www.sysbio.org/dataresources/cabin.stm . mudita.singhal@pnl.gov.

Community Detection in Biological Networks

Contributors

In this paper, aimed at the problem of detecting closed frequent subgraphs in biological networks, an improved FP-growth algorithm MaxFP is presented, which is based on the simplification model appropriate to biological networks. The defects of the algorithm based on item-set mining are analyzed when it is applied to biological networks, and which is overcome in MaxFP. In addition, MaxFP also takes the biological network characteristics into account. Experiment results show that MaxFP runs faster than the algorithms based on Apriori, and MaxFP not only detects maximal frequent subgraphs, but also finds more frequent subgraphs having biological meaning. The results got by performing Apriori based algorithms many times can be got by performing MaxFP once.

BackgroundHuman prion diseases are caused by abnormal accumulation of misfolded prion protein in the brain tissue. Inherited prion diseases, including familial Creutzfeldt-Jakob disease (fCJD), are associated with mutations of the prion protein gene (PRNP). The glutamate (E)-to-lysine (K) substitution at codon 200 (E200K) in PRNP is the most common pathogenic mutation causing fCJD, but the E200K pathogenic mutation alone is regarded insufficient to cause prion diseases; thus, additional unidentified factors are proposed to explain the penetrance of E200K-dependent fCJD. Here, exome differences and biological network analysis between fCJD patients with E200K and healthy individuals, including a non-CJD individual with E200K, were analysed to gain new insights into possible mechanisms for CJD in individuals carrying E200K.MethodsExome sequencing of the three CJD patients with E200K and 11 of the family of one patient (case1) were performed using the Illumina HiSeq 2000. The exome sequences of 24 Healthy Koreans were used as control. The bioinformatic analysis of the exome sequences was performed using the CLC Genomics Workbench v5.5. Sanger sequencing for variants validation was processed using a BigDye Terminator Cycle Sequencing Kit and an ABI 3730xl automated sequencer. Biological networks were created using Cytoscape (v2.8.3 and v3.0.2) and Pathway Studio 9.0 software.ResultsNineteen sites were only observed in healthy individuals. Four proteins (NRXN2, KLKB1, KARS, and LAMA3) that harbour rarely observed single-nucleotide variants showed biological interactions that are associated with prion diseases and/or prion protein in our biological network analysis.ConclusionThrough this study, we confirmed that individuals can have a CJD-free life, even if they carry a pathogenic E200K mutation. Our research provides a possible mechanism that involves a candidate protective factor; this could be exploited to prevent fCJD onset in individuals carrying E200K.

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

With the development of bioinformatics, genomics and high-throughput technology provides a lot of life system components ( such as proteins ) and the relationship between them in the data, these complex biological networks contain with rich life knowledge of moving mechanism of system, mining these covert knowledge become the one of the main task in the post genome era. However, currently the research of the biological network diagram structure has focused on static analysis and massive small pattern analysis, there is no effective dynamic analysis models and algorithms to meet the mass of structure dynamic analysis and data mining. In this paper, based on the existing graph analysis of data mining method, through the construction of a set of processing biological network analysis model to dynamic analysis and processing graph structure, improve the mass of data analysis and processing efficiency. The results of the project has wide application prospect, and can be used for biological network analysis, as well as chemistry, Internet and related fields.

The identification of disease related genes plays essential roles in bioinformatics. To achieve this, many powerful machine learning methods have been proposed from various computational aspects, such as biological network analysis, classification, regression, deep learning, etc. Among them, deep learning based methods have gained big success in identifying disease related genes in terms of higher accuracy and efficiency. However, these methods rarely handle the following two issues very well, which are (1) the multifunctions of many genes; and (2) the scale-free property of biological networks. To overcome these, we propose a novel network representation method to transfer individual vertices together with their surrounding topological structures into image-like datasets. It takes each node-induced sub-network as a represented candidate, and adds its environmental characteristics to generate a low-dimensional space as its representation. This image-like datasets can be applied directly in a Convolutional Neural Network-based method for identifying cancer-related genes. The numerical experiments show that the proposed method can achieve the AUC value at 0.9256 in a single network and at 0.9452 in multiple networks, which outperforms many existing methods.

The theory of complex networks has a wide range of applications in a variety of disciplines such as communications and power system engineering, the internet and worldwide web (www), food webs, human social networks, molecular biology, population biology and biological networks. The focus of this paper is on biological applications of the theory of graphs and networks. Graph theory and several graph theoretic properties serve as an ideal mathematical tool in the analysis of complex networks. We present the basic concepts and notations from graph theory which is widely used in the study of biological networks. Various biological networks such as Protein interaction networks, Metabolome based reaction network, Gene regulatory network, Gene coexpression network, Protein structure network, Structural brain network, Phylogenetic networks, Ecological networks and Food web networks are described. We also deal with various centrality measures which provide deep insight in the study of biological networks. Applications of biological network analysis in several areas are also discussed.

Network analysis is a powerful strategy for uncovering complex relationships in high-throughput omics datasets. However, current tools often lack scalability, flexibility, and native support for multi-omics integration, posing significant barriers for exploring complex biological networks. To address these limitations, we developed MetaNet, a high-performance R package designed to construct, visualize, and analyze biological networks from multi-omics datasets. MetaNet supports highly efficient correlation-based network construction, scalable to datasets with over 10,000 features, and includes extensive layout algorithms and visualization options compatible with both static and interactive platforms. It also provides a comprehensive suite of topological and stability metrics for in-depth network characterization. Benchmarking results show that MetaNet outperforms existing R packages by up to 100-fold in computation time and reduces memory usage by up to 50-fold. We demonstrate its utility through two case studies: (1) a longitudinal analysis of microbial co-occurrence networks showing the dynamics of the airborne microbiome, and (2) an integrative exposome–transcriptome network of more than 40,000 features, uncovering distinct regulatory impacts of biological and chemical exposures. MetaNet bridges the gap between network theory and omics application by offering a robust, reproducible, and biologically informed framework for large-scale, interpretable, and integrative network analyses across diverse omics platforms, advancing systems-level understanding in modern life sciences. MetaNet is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/MetaNet).

Introduction: Colorectal cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide. The rat sarcoma/mitogen-activated protein kinase (RAS/MAPK) signaling pathway plays a pivotal role in the regulation of cellular proliferation, differentiation, and survival, and its dysregulation is strongly associated with colorectal tumorigenesis. Recent studies suggest that microRNAs (miRNAs) critically influence cancer progression by targeting key components of this pathway. Objectives: This study aimed to systematically investigate dysregulated miRNAs that target the RAS/MAPK signaling pathway in colorectal cancer and identify core regulatory genes, enriched biological functions, and potential therapeutic targets using a systems biology approach. Materials and Methods: In this in silico study, differentially expressed miRNAs in colorectal cancer were identified from comprehensive literature and validated via the miRDB database (score >90). High-confidence target genes were analyzed using STRING to construct protein-protein interaction (PPI) networks. Hub genes were detected using CytoHubba in Cytoscape through multiple centrality algorithms (MNC, MCC, DMNC, Degree). Gene ontology (GO), KEGG, and transcription factor enrichment were performed using Enrichr. Promoter motif analysis was conducted via Tomtom and GOMO, and DrugBank was used to evaluate druggability of hub proteins. Results: Several hub proteins, including STAT3, JUN, PTEN, ESR1, KRAS, and HIF1A, were identified as central components of the miRNA-target network. Gene ontology and KEGG enrichment highlighted their roles in transcriptional regulation, MAPK cascade, and cell proliferation. Promoter analysis revealed key regulatory motifs, while DrugBank analysis identified several approved and investigational compounds targeting these hub proteins. Conclusion: The integration of miRNA –gene interaction analysis with network biology and drug discovery tools provided novel insights into the regulatory mechanisms underlying colorectal cancer. Hub proteins identified in this study show potential for targeted therapy and biomarker development. This study highlights critical miRNAs regulating the RAS/MAPK pathway in colorectal cancer and provides a framework for therapeutic target prioritization through integrative network analysis.

Modelling biological networks is inherently data-driven and data-intensive. The combinatorial nature of this type of modelling, however, requires new methods capable of dealing with the enormous size and irregularity of the search. Searching via ‘backtracking’ is one possible solution that avoids exhaustive searches by constraining the search space to the subspace of feasible solutions. Despite its wide use in many combinatorial optimization problems, there are currently few parallel implementations of backtracking capable of effectively dealing with the memory-intensive nature of the process and the extremely unbalanced loads present. In this paper, a parallel, scalable, and memory-efficient backtracking algorithm within the context of maximal clique enumeration is presented, and its applicability to large-scale biological networks aimed at studying the mechanisms for efficient bioethanol production is discussed.

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer’s disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named “FastGGM”.