Network Consistency Projection for Human miRNA-Disease Associations Inference

Gu, Changlong; Liao, Bo; Li, Xiaoying; Li, Keqin

doi:10.1038/srep36054

Cited by 94 publications

(55 citation statements)

References 55 publications

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“…The broad target spectrum of a single miR makes it is difficult to predict the consequence of miR regulation on cellular processes unless miR-regulated gene and protein expression are considered. Systems biology approaches successfully map such interactome and miRNome data for the identification of regulatory pathways in disease (23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Figure 2 Isolation Of Live Cd4 + Th Cell Subsets From Allermentioning

confidence: 99%

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation

et al. 2018

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Allergic asthma is a chronic inflammatory disease dominated by a CD4+ T helper 2 (Th2) cell signature. The immune response amplifies in self-enforcing loops, promoting Th2-driven cellular immunity and leaving the host unable to terminate inflammation. Posttranscriptional mechanisms, including microRNAs (miRs), are pivotal in maintaining immune homeostasis. Since an altered expression of various miRs has been associated with T cell-driven diseases, including asthma, we hypothesized that miRs control mechanisms ensuring Th2 stability and maintenance in the lung. We isolated murine CD4+ Th2 cells from allergic inflamed lungs and profiled gene and miR expression. Instead of focusing on the magnitude of miR differential expression, here we addressed the secondary consequences for the set of molecular interactions in the cell, the interactome. We developed the Impact of Differential Expression Across Layers, a network-based algorithm to prioritize disease-relevant miRs based on the central role of their targets in the molecular interactome. This method identified 5 Th2-related miRs (mir27b, mir206, mir106b, mir203, and mir23b) whose antagonization led to a sharp reduction of the Th2 phenotype. Overall, a systems biology tool was developed and validated, highlighting the role of miRs in Th2-driven immune response. This result offers potentially novel approaches for therapeutic interventions.

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Section: Figure 2 Isolation Of Live Cd4 + Th Cell Subsets From Allermentioning

confidence: 99%

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation

et al. 2018

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“…To quantify the predictive power of their network-based model, many studies use the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), another frequently used method in validation problems [39,87,88]. The AUC-ROC is the plot between sensitivity and (1-specificity).…”

Section: Methodsmentioning

confidence: 99%

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

et al. 2018

Preprint

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Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes.One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks. Data Science Pipelineimprove the classification of diseases [2]. However, the use of these sources raised new problems such as their fragmentation, heterogeneity, availability and different conceptualization of their data [3,4].Recent developments in network theory provide a way to address this challenge by representing these complex relationships as a collection of linked nodes [5]. Complex networks theory is a statistical physics interpretation of the old graph theory, aimed at describing and understanding the structures created by the relationships between the elements of a complex system [6][7][8][9]. Those elements are represented by nodes, pairwise connected by links whenever a relationship is observed between the corresponding elements. The resulting structure can then be described by means of a plethora of topological metrics [10], or be used as a base for modelling the system. The application of this field to biological problems has been named "network biology", while its use in biomedical problems is known as "network medicine" [11]. Some applications of biological networks are protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network or phylogenetic trees [12].Following this approach, disease networks express the relationship between diseases as nodes and edges in a graph in, where D represents the set of diseases (nodes) and W the set of their relationships (edges) based upon their similarity. The meaning of similarity varies depending on the data used to build the network, which may be biological (genes or common proteins) or phenotypic (comorbidity, similar symptoms) [13], among other approaches. As will be explained throughout this article, the concept of disease network is not limited to diseasedisease connections (homogeneous networks), but also to relations between the disease and other factors such as its symptoms, its associ...

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“…27 The data set is successfully applied to multiple methods. 80,[93][94][95] We use matrix SM to represent the adjacency matrix of miRNA, and SM(i, j) is the score of functional similarity score between miRNA i and miRNA j.…”

Section: Data Preparationmentioning

confidence: 99%

A novel information diffusion method based on network consistency for identifying disease related microRNAs

Chen

Peng

et al. 2018

RSC Adv.

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Network Consistency Projection for Human miRNA-Disease Associations Inference

Cited by 94 publications

References 55 publications

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

A novel information diffusion method based on network consistency for identifying disease related microRNAs

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