Search results for "ECoG"
showing 10 items of 3774 documents
Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions
2018
This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.
Contribution of the commensal microbiota to atherosclerosis and arterial thrombosis
2018
The commensal gut microbiota is an environmental factor that has been implicated in the development of cardiovascular disease. The development of atherosclerotic lesions is largely influenced not only by the microbial-associated molecular patterns of the gut microbiota but also by the meta-organismal trimethylamine N-oxide pathway. Recent studies have described a role for the gut microbiota in platelet activation and arterial thrombosis. This review summarizes the results from gnotobiotic mouse models and clinical data that linked microbiota-induced pattern recognition receptor signalling with atherogenesis. Based on recent insights, we here provide an overview of how the gut microbiota cou…
Establishing and validating a new source analysis method using phase.
2017
Electroencephalogram (EEG) measures the brain oscillatory activity non-invasively. The localization of deep brain generators of the electric fields is essential for understanding neuronal function in healthy humans and for damasking specific regions that cause abnormal activity in patients with neurological disorders. The aim of this study was to test whether the phase estimation from scalp data can be reliably used to identify the number of dipoles in source analyses. The steps performed included: i) modeling different phasic oscillatory signals using auto-regressive processes at a particular frequency, ii) simulation of two different noises, namely white and colored noise, having differen…
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
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 …
Newly Digitized Database Reveals the Lives and Families of Forced Migrants from Finnish Karelia
2017
Studies on displaced persons often suffer from a lack of data on the long-term effects of forced migration. A register created during 1960s and published as a book series ‘Siirtokarjalaisten tie’ in 1970 documented the lives of individuals who fled the southern Karelian district of Finland after its first and second occupation by the Soviet Union in 1940 and 1944. To realize the potential value of these data for scientific research, we have recently scanned the register using optical character recognition (OCR) software, and developed proprietary computer code to extract these data. Here we outline the steps involved in the digitization process, and present an overview of the Migration Kare…
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…
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…