Search results for "pattern"
showing 10 items of 4203 documents
MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment
2019
High-throughput metabolomics using liquid chromatography and mass spectrometry (LC/MS) provides a useful method to identify biomarkers of disease and explore biological systems. However, the majority of metabolic features detected from untargeted metabolomics experiments have unknown ion signatures, making it critical that data should be thoroughly quality controlled to avoid analyzing false signals. Here, we present a postalignment method relying on intermittent pooled study samples to separate genuine metabolic features from potential measurement artifacts. We apply the method to lipid metabolite data from the PREDIMED (PREvención con DIeta MEDi-terránea) study to demonstrate clear remova…
Mutations in the GLA Gene and LysoGb3: Is It Really Anderson-Fabry Disease?
2018
Anderson-Fabry disease (FD) is a rare, progressive, multisystem storage disorder caused by the partial or total deficit of the lysosomal enzyme &alpha
Fold formation at the compartment boundary of Drosophila wing requires Yki signaling to suppress JNK dependent apoptosis
2016
AbstractCompartment boundaries prevent cell populations of different lineage from intermingling. In many cases, compartment boundaries are associated with morphological folds. However, in the Drosophila wing imaginal disc, fold formation at the anterior/posterior (A/P) compartment boundary is suppressed, probably as a prerequisite for the formation of a flat wing surface. Fold suppression depends on optomotor-blind (omb). Omb mutant animals develop a deep apical fold at the A/P boundary of the larval wing disc and an A/P cleft in the adult wing. A/P fold formation is controlled by different signaling pathways. Jun N-terminal kinase (JNK) and Yorkie (Yki) signaling are activated in cells alo…
Automated selection of homologs to track the evolutionary history of proteins
2018
Background The selection of distant homologs of a query protein under study is a usual and useful application of protein sequence databases. Such sets of homologs are often applied to investigate the function of a protein and the degree to which experimental results can be transferred from one organism to another. In particular, a variety of databases facilitates static browsing for orthologs. However, these resources have a limited power when identifying orthologs between taxonomically distant species. In addition, in some situations, for a given query protein, it is advantageous to compare the sets of orthologs from different specific organisms: this recursive step-wise search might give …
Starvation resistance and tissue-specific gene expression of stress-related genes in a naturally inbred ant population
2016
Starvation is one of the most common and severe stressors in nature. Not only does it lead to death if not alleviated, it also forces the starved individual to allocate resources only to the most essential processes. This creates energetic trade-offs which can lead to many secondary challenges for the individual. These energetic trade-offs could be exacerbated in inbred individuals, which have been suggested to have a less efficient metabolism. Here, we studied the effect of inbreeding on starvation resistance in a natural population of Formica exsecta ants, with a focus on survival and tissue-specific expression of stress, metabolism and immunity-related genes. Starvation led to large tis…
Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences
2018
Several recent works have shown that K-mer sequence representation of a DNA sequence can be used for classification or identification of nucleosome positioning related sequences. This representation can be computationally expensive when k grows, making the complexity in spaces of exponential dimension. This issue effects significantly the classification task computed by a general machine learning algorithm used for the purpose of sequence classification. In this paper, we investigate the advantage offered by the so-called Variable Ranking Feature Selection method to select the most informative k − mers associated to a set of DNA sequences, for the final purpose of nucleosome/linker classifi…
No diagnostic utility of antibody patterns against Klebsiella pneumoniae capsular serotypes in patients with axial spondyloarthritis vs. patients wit…
2017
OBJECTIVES: To investigate whether antibody response patterns against Klebsiella pneumoniae capsular serotypes can discriminate patients with axial spondyloarthritis (axSpA) from patients with non-specific low back pain (LBP).METHOD: Immunoglobulin (Ig)G and IgA antibodies against K. pneumoniae capsular serotypes K2, K26, K36, and K50 were measured, and antibody seropositivity compared between groups and analysed for patient correlation in five different groups: (a) 96 patients fulfilling the Assessment of SpondyloArthritis International Society (ASAS) classification criteria for axSpA; (b) 38 patients with either a positive magnetic resonance imaging (MRI) scan as defined by ASAS or a posi…
Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas
2018
In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.
Discovering discriminative graph patterns from gene expression data
2016
We consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/unhealthy samples of an input dataset. We present an approach based on a network model of the input gene expression data, where there is a labelled graph for each sample. To the best of our knowledge, this is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. Our main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of "discriminative patterns" among graphs belonging to the two different sample sets. Differently from the other…
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…