Positional analysis in cross-media information diffusion networks

Hecking, Tobias; Steinert, Laura; Masías, Víctor Hugo; Hoppe, H. Ulrich

doi:10.1007/s41109-018-0108-x

Cited by 21 publications

(3 citation statements)

References 27 publications

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“…For this, we used mutual information (MI) as a measure of miR-gene co-expression. MI has been widely used for the reconstruction of co-expression networks [5,[18][19][20][21][22]. In previous work by our group, we have successfully reconstructed miR-gene co-expression networks using this approach [9,14].…”

Section: Microrna-gene Bipartite Network Reconstructionmentioning

confidence: 99%

Highly connected, non-redundant microRNA functional control in breast cancer molecular subtypes

2021

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Breast cancer is a complex, heterogeneous disease at the phenotypic and molecular level. In particular, the transcriptional regulatory programs are known to be significantly affected and such transcriptional alterations are able to capture some of the heterogeneity of the disease, leading to the emergence of breast cancer molecular subtypes. Recently, it has been found that network biology approaches to decipher such abnormal gene regulation programs, for instance by means of gene co-expression networks, have been able to recapitulate the differences between breast cancer subtypes providing elements to further understand their functional origins and consequences. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding transcripts such as microRNAs (miRs). As is known, miRs play relevant roles in the establishment of normal and anomalous transcription processes. Commodore miRs (cdre-miRs) have been defined as miRs that, based on their connectivity and redundancy in co-expression networks, are potential control elements of biological functions. In this work, we reconstructed miR–gene co-expression networks for each breast cancer molecular subtype, from high throughput data in 424 samples from the Cancer Genome Atlas consortium. We identified cdre-miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR was linked to a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer such as angiogenesis, cell adhesion, cell cycle and regulation of apoptosis. The relevance of such cdre-miRs as actionable molecular targets in breast cancer is still to be determined from functional studies.

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Section: Microrna-gene Bipartite Network Reconstructionmentioning

confidence: 99%

Highly connected, non-redundant microRNA functional control in breast cancer molecular subtypes

2021

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“…Following Ref. [24], each node i ∈ V is characterized by an activity parameter a i ∈ [0, 1]. At each time, node i activates with probability a i and generates an undirected link with another node.…”

Section: A Routed Adnsmentioning

confidence: 99%

“…In their fundamental incarnation, ADNs are an ideal tool to model weak ties, whereby the whole process of network assembly is driven by a node-specific attribute, the activity. Routed ADNs (RADNs) have been recently proposed to include strong ties within the ADN paradigm [23,24]. In this model, temporal connections are wired according to a stochastic rule that encapsulates both the topological information of strong ties and the unstructured connections of weak ties.…”

Section: Introductionmentioning

confidence: 99%

Backbone reconstruction in temporal networks from epidemic data

et al. 2019

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Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.

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