Information theoretical methods for complex network structure reconstruction

Hernández‐Lemus, Enrique; Siqueiros-García, J. Mario

doi:10.1186/2194-3206-1-8

Cited by 24 publications

(16 citation statements)

References 43 publications

(47 reference statements)

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“…In practice doing this is rather difficult because one is faced with large numbers of variables with a strong nonlinear behavior. Mutual Information ( MI ) is a measure from information theory that is able to deal with these issues since it is model independent, non-parametric and capable of capturing non-linear dependencies (Hernández-Lemus and Siqueiros-García, 2013). …”

Section: Methodsmentioning

confidence: 99%

“…Transcriptional network inference based on MI has been successfully employed (Hernández-Lemus and Siqueiros-García, 2013; Khosravi et al, 2015; Rodriguez-Barrueco et al, 2015). (Margolin et al, 2006) is one of many algorithms used to calculate MI based on gene expression.…”

Section: Methodsmentioning

confidence: 99%

“…We must notice, however, that this correlation structure is usually given in the presence of an accompanying variance structure among gene expression levels (Giuliani et al, 2004). It has long been proven that the best estimator of statistic dependency is mutual information (MI) (Basso et al, 2005; Margolin et al, 2006; Hernández-Lemus and Rangel-Escareño, 2011; de Matos Simoes and Emmert-Streib, 2012; Hernández-Lemus and Siqueiros-García, 2013). These statistically inferred networks provide a deeper level of biological understanding in two main directions: giving support to previously identified biological observations, and giving new insights regarding novel biological interactions.…”

Section: Introductionmentioning

confidence: 99%

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Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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“…For this network 60 interactions were kept if its p value was below 0.005. Given that mutual information 61 can detect both indirect and direct relationships, ARACNe limits the number of indirect 62 interactions applying the Data Processing Inequality theorem (DPI), which considers 63 that, in a triangle of interactions, the weakest one has a greater probability of being 64 indirect if its difference is large with respect to the other two [22]. We applied a DPI 65 value of 0.2 as recommended in Margolin et al 2006 [19], which means that the weakest 66 interactions of the triangles in the network were eliminated without introducing an 67 excessive number of false positives.…”

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confidence: 99%

Master regulators of signaling pathways coordinate key processes of embryonic development in breast cancer

Tapia-Carrillo

Tovar

et al. 2018

Preprint

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Signal transduction pathways allow cells to respond to environmental cues and can induce intracellular changes. In some contexts, like embryonic development, signal transduction plays crucial roles in cell fate determination and differentiation, while in developed organisms some of this processes contribute in the maintenance of the structural integrity of tissues.Tumor cells are recognized as having deregulated signaling which leads to a series of abnormal behaviors known as the hallmarks of cancer. Although gene regulation is often viewed as the last step in signal transduction, transcriptional regulation of the components of a pathway may impact in the long term deregulation observed in tumors. The study of gene regulatory networks centered around genes of the signal transduction pathways allows the identification of transcriptional regulators with the greatest influence over the signal transduction gene signature, also denominated Master Regulators.In this work we identify, the master regulators that regulate the expression of genes of 25 relevant pathways grouped in KEGG within the category of signal transduction in a breast cancer dataset. For this purpose we implemented a modified MARINa algorithm that identifies, from a network of regulons, those that possess more differentially expressed genes related to the process to be studied. We identified CLOCK, TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3 and GLI2 as the top 10 master regulators of signaling pathways in breast cancer. Nine of them are recognized for taking part in embryonic development associated processes.Individual enrichment GO biological function for each TMR regulons showed to be significantly enriched in embryonic development related processes. Hedgehog signaling pathway was shown as enriched and also highly deregulated. The genes of the HOXA family are shared among most of the TMRs. Overall, this suggests the importance of the aberrant reprogramming of mechanisms present during embryonic development, being coopted in favor of tumor development.

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“…The analysis of cooperation in scientific research has been the subject of a number of studies (Vermeulen et al 2013;Newman 2001Newman , 2004Elango and Rajendran 2012;Hernández-Lemus and Siqueiros-García 2013;Strasser 2006Strasser , 2012. This is not surprising since cooperation and competition are quite important in today's academic success.…”

mentioning

confidence: 99%

A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks

Siqueiros-García

García-Herrera

Hernández‐Lemus

et al. 2017

Complex Adapt Syst Model

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Purpose Collaboration has become a cornerstone in biomedical research today. In contrast to physics which has a long history and experience in collaborative projects, biology is only recently becoming an evermore collaborative discipline. In this article we explore the effect of a collaboration network on the distribution of players having access to certain amount of resources from other players in the network and the distribution of the strength of interactions among them. We are interested in how they affect each other in the context of a network of scientific collaboration under the idea that while researchers are interested in maximizing their utilities, they also know that it is important to invest in building collaborative relationships. Methods We implemented two games played simultaneously: one for maximizing individual utility based on the iterated prisoner’s dilemma; the other, a coordination game for maximizing the connection strength between players. We tested our simulation on a biomedical research community network in México and compared the results with Erdös–Renyí, a Watts–Strogatz small-world and Barabási–Albert topologies. Results Different topologies display different global utility and global strength of interaction distributions. Moreover, the distribution of utility and strength of interaction in the researchers network is similar to that of Barabási–Albert and Watts–Strogatz topologies, respectively. Conclusions Data related to Science, from co-authorships to Scientists' movility are increasingly becoming available. We think that the readiness of these sort of data is a great opportunity for scientists interested in the social dynamics of science, especially in the context of computational social science.

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Information theoretical methods for complex network structure reconstruction

Cited by 24 publications

References 43 publications

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Master regulators of signaling pathways coordinate key processes of embryonic development in breast cancer

A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks

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