scholarly journals Parallelisable Existential Rules: a Story of Pieces

2021 ◽  
Author(s):  
Maxime Buron ◽  
Marie-Laure Mugnier ◽  
Michaël Thomazo
Keyword(s):  
Data Integration ◽  
Rule Composition ◽  
Rule Sets

In this paper, we consider existential rules, an expressive formalism well adapted to the representation of ontological knowledge, as well as data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.

scholarly journals Remote sensing and seismic data integration for the characterization of a rock slide and an artificially triggered rock fall

2019 ◽  
Vol 257 ◽  
pp. 105113 ◽  
Author(s):  
Marta Guinau ◽  
Mar Tapia ◽  
Cristina Pérez-Guillén ◽  
Emma Suriñach ◽  
Pere Roig ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Seismic Data ◽  
Rock Fall ◽  
Rock Slide

scholarly journals Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine

2014 ◽  
Vol 24 (2) ◽  
pp. 81-93 ◽  
Author(s):  
Michael Harris ◽  
Krithika Bhuvaneshwar ◽  
Thanemozhi Natarajan ◽  
Laura Sheahan ◽  
Difei Wang ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Data Integration

scholarly journals Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing

2020 ◽  
Author(s):  
Luyi Tian ◽  
Jafar S. Jabbari ◽  
Rachel Thijssen ◽  
Quentin Gouil ◽  
Shanika L. Amarasinghe ◽  
...  
Keyword(s):  
Data Integration ◽  
Single Cell ◽  
Ribosome Biogenesis ◽  
Single Cells ◽  
Transcript Level ◽  
Full Length ◽  
Alternative Transcript ◽  
Long Read ◽  
Comprehensive Characterization

AbstractAlternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

Mapping and characterization of thin chalk reservoirs using data integration: the Kraka Field, Danish North Sea

2005 ◽  
Vol 11 (2) ◽  
pp. 113-124 ◽  
Author(s):  
Lone Klinkby ◽  
Lars Kristensen ◽  
Erik B. Nielsen ◽  
Kim Zinck-Jørgensen ◽  
Lars Stemmerik
Keyword(s):  
Data Integration ◽  
North Sea ◽  
Using Data

scholarly journals Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Mario Zanfardino ◽  
Monica Franzese ◽  
Katia Pane ◽  
Carlo Cavaliere ◽  
Serena Monti ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Structures ◽  
R Package ◽  
Biological Information ◽  
Phenotype Definition ◽  
Current State ◽  
Oncological Diseases ◽  
Cancer Phenotype

Abstract Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

Spatial data integration for the environmental characterization of pasture macrotypes in the Italian Alps

2015 ◽  
Vol 71 (2) ◽  
pp. 219-234 ◽  
Author(s):  
C. Dibari ◽  
M. Bindi ◽  
M. Moriondo ◽  
N. Staglianò ◽  
S. Targetti ◽  
...  
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Italian Alps ◽  
Environmental Characterization ◽  
Spatial Data Integration

scholarly journals Reproducibility and reusability limitations in Regulatory Circuits: analysis and solutions

2021 ◽  
Author(s):  
Marine Louarn ◽  
Anne Siegel ◽  
Thierry Fest ◽  
Olivier Dameron ◽  
Fabrice Chatonnet
Keyword(s):  
Data Integration ◽  
Regulatory Networks ◽  
Cell Types ◽  
Biological Data ◽  
Google Scholar ◽  
Cell Type ◽  
Regulatory Circuits ◽  
Cell Type Specific ◽  
Different Levels

The Regulatory Circuits project is among the most recent and the most complete attempts to identify cell-type specific regulatory networks in Human. It is one of the largest efforts of public genomics data integration, based on data from the major consortia FANTOM5, ENCODE and Roadmap Epigenomics. This project is a main provider of biological data, cited more than 224 times (Google Scholar) and its resulting networks were used in at least 42 other articles. For such a general resource, reproducibility of both the outputs (regulation networks) and methods (data integration pipeline) is a major issue, since biological data are updated regularly. In addition, users may want to introduce new data into the Regulatory Circuits framework to provide networks about previously uncharacterized cell types or to add information about specific regulators, which require to re-execute the whole pipeline on the new data. In this article, we analyze the various factors limiting reproducibility of the Regulatory Circuits data and methods. Starting from a factual description of our understanding of the methods used in Regulatory Circuits, our contribution is two-fold: we propose (1) a characterization of the different levels of reusability, reproducibility and conceptual issues in the original workflow and (2) a new implementation of the workflow ensuring its consistency with the published description and allowing for an easier reuse and reproduction of the published outputs. Both are applicable beyond the case of Regulatory Circuits.

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

2009 ◽  
Vol 24 (5) ◽  
pp. 735-749 ◽  
Author(s):  
Amir H. Hosseini ◽  
Clayton V. Deutsch ◽  
Kevin W. Biggar ◽  
Carl A. Mendoza
Keyword(s):  
Spatial Distribution ◽  
Data Integration ◽  
Probabilistic Data
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