VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies

Hu, Zhenjun; Chang, Yi-Chien; Wang, Yan; Huang, Chia‐Ling; Liu, Yang; Tian, Feng; Granger, Brian R.; DeLisi, Charles

doi:10.1093/nar/gkt401

Cited by 124 publications

(109 citation statements)

References 30 publications

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“…Modules containing ≥30 genes were included in our analyses. For module visualization, we used the publically available VisANT software (Hu et al 2013). To determine the reliability of the WGCNA module characterization and the DEGs, we performed a permutation test randomizing 1000 times the expression data associated with each gene, calculated the DEGs, and then applied the same module characterization.…”

Section: Rna-seq Data Analysismentioning

confidence: 99%

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum

et al. 2015

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Mutations in the transcription factor Forkhead box p1 (FOXP1) are causative for neurodevelopmental disorders such as autism. However, the function of FOXP1 within the brain remains largely uncharacterized. Here, we identify the gene expression program regulated by FoxP1 in both human neural cells and patient-relevant heterozygous Foxp1 mouse brains. We demonstrate a role for FoxP1 in the transcriptional regulation of autism-related pathways as well as genes involved in neuronal activity. We show that Foxp1 regulates the excitability of striatal medium spiny neurons and that reduction of Foxp1 correlates with defects in ultrasonic vocalizations. Finally, we demonstrate that FoxP1 has an evolutionarily conserved role in regulating pathways involved in striatal neuron identity through gene expression studies in human neural progenitors with altered FOXP1 levels. These data support an integral role for FoxP1 in regulating signaling pathways vulnerable in autism and the specific regulation of striatal pathways important for vocal communication.

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Section: Rna-seq Data Analysismentioning

confidence: 99%

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum

et al. 2015

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“…Researchers are now developing approaches that analyze genetic associations in the context of networks and pathways to prioritize potential drugs (16)(17)(18)(19)(20)(21). There is not yet a standard approach or set of techniques that are widely applied, so we highlight examples of the types of techniques being employed.…”

Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Greene

Voight

2016

Hum. Mol. Genet.

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One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.

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“…Metagraphs are less restricted than compound graphs and allow representing biological networks more accurately. They have been used to visualize cellular networks at different granularitiesfrom high level networks among groups of molecules to low level networks describing the relationships among individual molecules within and outside of each group [57,58].…”

Section: Metagraphsmentioning

confidence: 99%

Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies

Cited by 124 publications

References 30 publications

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Unstable Communities in Network Ensembles

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