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
Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer
Pamela Vernocchi,Tommaso Gili,Federica Conte,Federica Del Chierico,Giorgia Conta,Alfredo Miccheli,Andrea Botticelli,Andrea Botticelli,Paola Paci,Guido Caldarelli,Guido Caldarelli,Marianna Nuti,Paolo Marchetti,Paolo Marchetti,Lorenza Putignani +14 more
TL;DR: It is demonstrated that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients as well as identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer patients undergoing second-line treatment with anti-PD1.
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Prediction and early diagnosis of complex diseases by edge-network
TL;DR: The edge-network analysis not only opens a new way to understand pathogenesis at a network level due to the new representation for a stochastic network, but also provides a powerful tool to make the early diagnosis of diseases.
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Network modeling of single-cell omics data: challenges, opportunities, and progresses.
TL;DR: An overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks and the outlooks of the field moving forward are offered.
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TET2 binds the androgen receptor and loss is associated with prostate cancer
Michael L. Nickerson,Shiva K. Das,K M Im,Sevilay Turan,Sonja I. Berndt,Hongchuan Li,Hongchuan Li,Hong Lou,Hong Lou,Seth A. Brodie,Seth A. Brodie,Jean-Noel Billaud,Tongwu Zhang,Aaron J. Bouk,Aaron J. Bouk,D Butcher,Zhao-Qi Wang,Lei Sun,K Misner,W Tan,W Tan,A Esnakula,Dominic Esposito,Wen Yi Huang,Robert N. Hoover,Margaret A. Tucker,Jonathan R. Keller,Joseph Boland,Joseph Boland,Kevin M. Brown,Stephen K. Anderson,Lee E. Moore,W. Isaacs,S. J. Chanock,Meredith Yeager,Meredith Yeager,Michael Dean,Thorkell Andresson +37 more
TL;DR: Low TET2 mRNA expression in TCGA PCa tumours is strongly associated with reduced patient survival, indicating reduced expression in tumours may be an informative biomarker of disease progression and perhaps metastatic disease.
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Microbial community structure and microbial networks correspond to nutrient gradients within coastal wetlands of the Laurentian Great Lakes.
TL;DR: This research provides an initial characterization of microbial communities among Great Lakes coastal wetlands and demonstrates that microbial communities could be negatively impacted by anthropogenic activities.
References
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