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Background
Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.
Methods
Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics...
Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of...
Early B cell development is characterized by large-scale Igh locus contraction prior to V(D)J recombination to facilitate a highly diverse Ig repertoire. However, an understanding of the molecular architecture that mediates locus contraction remains unclear. We have combined high-resolution chromosome conformation capture (3C) techniques with 3D DNA FISH to identify three conserved topological subdomains...
Transcription factors and microRNAs are both known to regulate gene expression in eukaryotes in a sequence-specific manner. This has led to the creation of numerous computational approaches that aim at predicting what genes are the targets of certain transcription factors and microRNAs. These methods, although powerful, provide a static snapshot of how genes may be regulated and are often plagued...
Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize transcription factors for regulatory functional studies. A novel approach based on the use of Lasso regression models is proposed to address this problem. We examine the...
Transcription factors and microRNAs are both considered pivotal regulators of gene expression. Numerous computational methods have been developed to predict their targets. These methods, although powerful, provide a static snapshot of how genes may be regulated by transcription factors and microRNAs. We propose a method that combines these prediction data with co-expression analysis and a supervised...
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