Inference of protein-protein interaction networks from multiple heterogeneous data.
Ontology highlight
ABSTRACT:
SUBMITTER: Huang L
PROVIDER: S-EPMC4761017 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.
| S-EPMC6337127 | biostudies-literature
Project description:In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of chronic obstructive pulmonary disease. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior.
| S-EPMC7672535 | biostudies-literature
Project description:Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions centre around focal adhesion kinase and tyrosine kinase substrate TKS4, and 14 interactions are supported by the literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs.
| S-EPMC4804358 | biostudies-literature
Project description:MotivationProtein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain-domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs.ResultsHere, we describe a novel method called K-GIDDI (knowledge-guided inference of DDIs) to narrow down the PPI sites to smaller regions/domains. K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges. Our results indicate that K-GIDDI can reliably predict DDIs. Most importantly, K-GIDDI's novel network expansion procedure allows prediction of DDIs that are otherwise not identifiable by methods that rely only on PPI data.Availabilityhttp://www.ittc.ku.edu/~xwchen/domainNetwork/ddinet.html
| S-EPMC2752622 | biostudies-literature
Project description:Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.
| S-EPMC9845406 | biostudies-literature
Project description:BackgroundThe accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.ResultsIn this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.ConclusionsIn this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.
| S-EPMC5773919 | biostudies-literature
Project description:MotivationProtein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.ResultsIn this study, we propose a new algorithm, NEOComplex ( NE CC- and O rtholog-based Complex identification by multiple network alignment), which integrates functional orthology information that can be obtained from different types of multiple network alignment (MNA) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient (NECC) to assign weights to interaction edges in PPI networks so that protein complexes can be identified more accurately. The NECC is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that our algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate edge loss in PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict.Availability and implementationhttp://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex.Contactbab@csail.mit.edu or csliao@ie.nthu.edu.tw.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive interpretation of the resulting networks and clearer guidance on potential targets for treatment, but also improves power for identifying the edges of highly connected nodes. Through simulations, we demonstrate the utility of our method and compare its performance to current popular methods that do not borrow information regarding hub nodes across networks. We illustrate the applicability of our method to inference of co-expression networks from The Cancer Genome Atlas ovarian carcinoma dataset.
| S-EPMC6393214 | biostudies-literature
Project description:There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional omics data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem and solve it with an alternating direction method of multipliers (ADMM) algorithm. The proposed method can also be adapted to estimate interaction networks for high dimensional compositional data such as microbial interaction networks. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease.
| S-EPMC8528283 | biostudies-literature
Project description:Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.
| S-EPMC9216621 | biostudies-literature