- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
PyGMQL: scalable data extraction and analysis for heterogeneous genomic datasets ; volume:20 ; number:1 ; day:8 ; month:11 ; year:2019 ; pages:1-11 ; date:12.2019
BMC bioinformatics ; 20, Heft 1 (8.11.2019), 1-11, 12.2019
- Creator
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s12859-019-3159-9
- URN
-
urn:nbn:de:101:1-2020030700165859952286
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:02 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Nanni, Luca
- Pinoli, Pietro
- Canakoglu, Arif
- Ceri, Stefano
- SpringerLink (Online service)
Other Objects (12)
Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets
Information extraction : towards scalable, adaptable systems
Scalable Concept Extraction in Industry 4.0
A scalable association rule learning heuristic for large datasets
Text mining on COVID19 datasets - Terminology extraction
SEDAR : A Semantic Data Reservoir for Heterogeneous Datasets
INTEGRATING HETEROGENEOUS DATASETS IN HBIM OF DECORATED SURFACES
PyPy status talk (a.k.a.: no no, PyPy is not dead)
Working with real world datasets : preprocessing and prediction with large incomplete and heterogeneous datasets
Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets
Information extraction : towards scalable, adaptable systems
Scalable Concept Extraction in Industry 4.0
A scalable association rule learning heuristic for large datasets
Text mining on COVID19 datasets - Terminology extraction
SEDAR : A Semantic Data Reservoir for Heterogeneous Datasets
INTEGRATING HETEROGENEOUS DATASETS IN HBIM OF DECORATED SURFACES
PyPy status talk (a.k.a.: no no, PyPy is not dead)
Working with real world datasets : preprocessing and prediction with large incomplete and heterogeneous datasets
Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets
Information extraction : towards scalable, adaptable systems
Scalable Concept Extraction in Industry 4.0
A scalable association rule learning heuristic for large datasets
Text mining on COVID19 datasets - Terminology extraction
SEDAR : A Semantic Data Reservoir for Heterogeneous Datasets
INTEGRATING HETEROGENEOUS DATASETS IN HBIM OF DECORATED SURFACES
PyPy status talk (a.k.a.: no no, PyPy is not dead)