Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease

Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder associated with allergic hypersensitivity to food. We interrogated >1.5 million genetic variants in European EoE cases and subsequently in a multi-site cohort with local and out-of-study control subjects. In addition to replication of the 5q22 locus (meta-analysis p = 1.9×10−16), we identified association at 2p23 (encoding CAPN14, p = 2.5×10−10). CAPN14 was specifically expressed in the esophagus, dynamically upregulated as a function of disease activity and genetic haplotype and after exposure of epithelial cells to IL-13, and located in an epigenetic hotspot modified by IL-13. There was enriched esophageal expression for the genes neighboring the top 208 EoE sequence variants. Multiple allergic sensitization loci were associated with EoE susceptibility (4.8×10−2 < p < 5.1×10−11). We propose a model that elucidates the tissue specific nature of EoE that involves the interplay of allergic sensitization with an EoE-specific, IL-13–inducible esophageal response involving CAPN14

Integration of spectral information into support vector machine for land cover classification

Support vector machines (SVM) have been widely used for classification purposes. These learning machines are based on classification of data through a kernel function. Classically these kernel functions are either based the Euclidean distance of two data vectors or their dot products. This is a general formulation which is suitable for most data sets. However, when dealing with remote sensing images, the addition of spectral information can add to the divisibility of the data and hence produce higher classification accuracy. In this paper, instead of the Euclidean distance we use the spectral angle function as a differentiation measure of two data vectors. The results show that using this method, high quality separation is achieved leading us to believe that integration of spectral information into the SVM method is indeed an effective approach

Composite kernels for support vector classification of hyper-spectral data

The incorporation of prior knowledge into the Support Vector Machine (SVM) architecture is a problem which if solved can lead to much more accurate classifiers in the near future. This result could be particularly effective in the classification of remote sensing imagery, where an abundance of information is available prior to classification. The most evident method to feed prior knowledge into the SVM algorithm is through the SVM kernel function. This paper proposes several composite kernel functions designed specifically for land cover classification of remote sensing imagery. These kernels make use of the spectral signature information, inherently available in remote sensing imagery. The results achieved from these kernels are very much satisfactory and surpass all previous results produced by classical kernels

A review of support vector machines with respect to spatial data

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples

Support vector classification of remote sensing images using improved spectral Kernels

A very important task in pattern recognition is the incorporation of prior information into the learning algorithm. In SUppOlt vector machines this task is performed via the kernel function. Thus for each application if the right kernel function is chosen, the amount of prior information fed into the machine is increased and thus the machine will perform with much more functionality. In the case of hyper-spectral imagery the amount of information available prior to classification is a vast amount. Current available kernels do not take full advantage of the amount of information available in these images. This paper focuses on deriving a set of kernels specific to these imagery. These kernels make use of the spectral signature available in images. Subsequently we use mixtures of these kernels to derive new and more efficient kernels for classification. Results show that these kernels do in fact improve classification accuracy and use the prior information available in imagery to a better degree

Spectral angle based kernels for the classification of hyperspectral images using support vector machines

Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature information that can be achieved from remote sensing images. Given the high value of this information, integrating it into the SVM algorithm is a reasonable suggestion. This paper utilizes the spectral angle (SA) function as a measure for classification of a hyperspectral image. The SA function is joined together with the radial basis function (RBF) to form a spectral angle based RBF function. Experimentation results are promising and confirm that this approach can compete with existing classification methods

Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease

Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder associated with allergic hypersensitivity to food. We interrogated >1.5 million genetic variants in European EoE cases and subsequently in a multi-site cohort with local and out-of-study control subjects. In addition to replication of the 5q22 locus (meta-analysis p = 1.9×10(−16)), we identified association at 2p23 (encoding CAPN14, p = 2.5×10(−10)). CAPN14 was specifically expressed in the esophagus, dynamically upregulated as a function of disease activity and genetic haplotype and after exposure of epithelial cells to IL-13, and located in an epigenetic hotspot modified by IL-13. There was enriched esophageal expression for the genes neighboring the top 208 EoE sequence variants. Multiple allergic sensitization loci were associated with EoE susceptibility (4.8×10(−2) < p < 5.1×10(−11)). We propose a model that elucidates the tissue specific nature of EoE that involves the interplay of allergic sensitization with an EoE-specific, IL-13–inducible esophageal response involving CAPN14