Search results for "computer.software_genre"
showing 10 items of 3858 documents
Methods for RNA Modification Mapping Using Deep Sequencing: Established and New Emerging Technologies
2019
New analytics of post-transcriptional RNA modifications have paved the way for a tremendous upswing of the biological and biomedical research in this field. This especially applies to methods that included RNA-Seq techniques, and which typically result in what is termed global scale modification mapping. In this process, positions inside a cell`s transcriptome are receiving a status of potential modification sites (so called modification calling), typically based on a score of some kind that issues from the particular method applied. The resulting data are thought to represent information that goes beyond what is contained in typical transcriptome data, and hence the field has taken to use …
Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures
2019
Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide re…
Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
2017
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…
WES/WGS Reporting of Mutations from Cardiovascular "Actionable" Genes in Clinical Practice: A Key Role for UMD Knowledgebases in the Era of Big Datab…
2016
International audience; High-throughput next-generation sequencing such as whole-exome and whole-genome sequencing are being rapidly integrated into clinical practice. The use of these techniques leads to the identification of secondary variants for which decisions about the reporting or not to the patient need to be made. The American College of Medical Genetics and Genomics recently published recommendations for the reporting of these variants in clinical practice for 56 "actionable" genes. Among these, seven are involved in Marfan Syndrome And Related Disorders (MSARD) resulting from mutations of the FBN1, TGFBR1 and 2, ACTA2, SMAD3, MYH11 and MYLK genes. Here, we show that mutations col…
Editorial: Protein Interaction Networks in Health and Disease
2016
The identification and annotation of protein-protein interactions (PPIs) is of great importance in systems biology. Big data produced from experimental or computational approaches allow not only the construction of large protein interaction maps but also expand our knowledge on how proteins build up molecular complexes to perform sophisticated tasks inside a cell. However, if we want to accurately understand the functionality of these complexes, we need to go beyond the simple identification of PPIs. We need to know when and where an interaction happens in the cell and also understand the flow of information through a protein interaction network. Another perspective of the research on PPI n…
Arm Hypervisor and Trustzone Alternatives
2020
Many scenarios such as DRM, payments, and homeland security require a trusted and verified trusted execution environment (TEE) on ARM. In most cases such TEE should be available in source code mode. The vendor cannot conduct code review and ensure that the operating system is trustworthy unless source code is available. Android and other rich execution environments (REEs) support various TEE implementations. Each TEE implementation has its own unique way of deploying trusted applications and features. Most TEEs in ARM can be started at TrustZone™ or Hyp (Hypervisor) mode. Choosing a proper TEE operating system can be a problem for trusted application developers and hardware vendors. This ar…
Grapes: a method and a SAS program for graphical representations of assessor performances
1994
GRAPES computes individual and global analyses of variance for sensory profiling data, consisting of several sessions in which all the panelists gave scores to all the products for a number of attributes. The fitted model takes into account the session effect. GRAPES summarizes the results by means of graphical assessor scatterplots which allow to check and to compare panelist performances, such as the way of using scale, the reliability, the discrimination power and the agreement with the panel. In addition, GRAPES detects the outliers for each of these criterion. The usefulness of GRAPES for the panel leader will be demonstrated using texture and flavor profiling of 4 restructured steaks …
Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power
2011
A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large biological datasets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call filters and integrators. These concepts are illustrated in four landmark studies that have provided model implementations of filters, integrators, or both.
Low-cost scalable discretization, prediction and feature selection for complex systems
2019
The introduced data-driven tool allows simultaneous feature selection, model inference, and marked cost and quality gains.
Comments from Pascal Schlich on the Steinsholt's paper
1998
International audience