Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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TLDR
A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.Abstract:
Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.read more
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Journal ArticleDOI
Integrated Systems Biology Approach Identifies Novel Maternal and Placental Pathways of Preeclampsia.
Nandor Gabor Than,Roberto Romero,Adi L. Tarca,Adi L. Tarca,Katalin A. Kékesi,Yi Xu,Zhonghui Xu,Zhonghui Xu,Kata Juhasz,Gaurav Bhatti,Ron Leavitt,Zsolt Gelencser,János Pálhalmi,Tzu Hung Chung,Balazs Gyorffy,László Orosz,Amanda Demeter,Anett Szecsi,Éva Hunyadi-Gulyás,Zsuzsanna Darula,Attila Simor,Katalin Éder,Szilvia Szabo,Szilvia Szabo,Vanessa Topping,Haidy El-Azzamy,Christopher LaJeunesse,Andrea Balogh,Andrea Balogh,Gabor Szalai,Gabor Szalai,Susan Land,Olga Török,Zhong Dong,Ilona Kovalszky,András Falus,Hamutal Meiri,Sorin Draghici,Sonia S. Hassan,Sonia S. Hassan,Tinnakorn Chaiworapongsa,Tinnakorn Chaiworapongsa,Manuel Krispin,Martin Knöfler,Offer Erez,Offer Erez,Offer Erez,Graham J. Burton,Chong Jai Kim,Chong Jai Kim,Chong Jai Kim,Gábor Juhász,Zoltán Papp +52 more
TL;DR: There are distinct maternal and placental disease pathways, and their interaction influences the clinical presentation of preeclampsia, and the description of these novel pathways in the “molecular phase” of preeClampsia and the identification of their hub molecules may enable timely molecular characterization of patients with distinct phenotypes.
Journal ArticleDOI
The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study.
Sian M. J. Hemmings,Stefanie Malan-Müller,Leigh van den Heuvel,Brittany A. Demmitt,Maggie A. Stanislawski,David G. Smith,Adam D. Bohr,Christopher E. Stamper,Embriette R. Hyde,James T. Morton,Clarisse Marotz,Philip H. Siebler,Maarten Braspenning,Wim Van Criekinge,Andrew J. Hoisington,Lisa A. Brenner,Teodor T. Postolache,Matthew B. McQueen,Kenneth Krauter,Rob Knight,Soraya Seedat,Christopher A. Lowry +21 more
TL;DR: In this paper, the association between the gut microbiota and posttraumatic stress disorder was investigated, and the results showed that inadequate immunoregulation and elevated inflammation may be risk factors for post-traumatic stress disorders.
Journal ArticleDOI
Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways
Karima Righetti,Joseph Ly Vu,Sandra Pelletier,Benoit Ly Vu,Enrico Glaab,David Lalanne,Asher Pasha,Rohan V. Patel,Nicholas J. Provart,Jerome Verdier,Olivier Leprince,Julia Buitink +11 more
TL;DR: The identification of a gene regulatory network related to seed longevity in both Medicago truncatula and Arabidopsis reveals a role for biotic defense-related genes in acquisition of longevity and suggests that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens.
Journal ArticleDOI
Alzheimer's Risk Factors Age, APOE Genotype, and Sex Drive Distinct Molecular Pathways.
Na Zhao,Yingxue Ren,Yu Yamazaki,Wenhui Qiao,Fuyao Li,Lindsey M. Felton,Siamak MahmoudianDehkordi,Alexandra Kueider-Paisley,Berkiye Sonoustoun,Matthias Arnold,Francis Shue,Jiaying Zheng,Olivia N. Attrebi,Yuka A. Martens,Zonghua Li,Ligia I. Bastea,Axel D. Meneses,Kai Chen,J. Will Thompson,Lisa St. John-Williams,Masaya Tachibana,Tomonori Aikawa,Hiroshi Oue,Lucy Job,Akari Yamazaki,Chia Chen Liu,Peter Storz,Yan W. Asmann,Nilufer Ertekin-Taner,Takahisa Kanekiyo,Rima Kaddurah-Daouk,Guojun Bu +31 more
TL;DR: It is found that age had the greatest impact on brain transcriptomes highlighted by an immune module led by Trem2 and Tyrobp, whereas APOE4 was associated with upregulation of multiple Serpina3 genes, and these networks and gene expression changes were mostly conserved in human brains.
Journal ArticleDOI
Biological impacts and context of network theory
TL;DR: It is expected that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction networks and metabolic networks will dramatically enhance the understanding of cellular function and dynamics.
References
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Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
Journal ArticleDOI
R: A Language for Data Analysis and Graphics
Ross Ihaka,Robert Gentleman +1 more
TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.
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