- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Predicting cancer involvement of genes from heterogeneous data ; volume:9 ; number:1 ; day:27 ; month:3 ; year:2008 ; pages:1-18 ; date:12.2008
BMC bioinformatics ; 9, Heft 1 (27.3.2008), 1-18, 12.2008
- Creator
-
Aragues, Ramon
- Contributor
-
Sander, Chris
Oliva, Baldo
SpringerLink (Online service)
- DOI
-
10.1186/1471-2105-9-172
- URN
-
urn:nbn:de:1111-201602171496
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:34 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
- Aragues, Ramon
- Sander, Chris
- Oliva, Baldo
- SpringerLink (Online service)
Other Objects (12)
Predicting cytotoxicity from heterogeneous data sources with Bayesian learning
Measuring and Predicting Heterogeneous Recessions
Measuring and Predicting Heterogeneous Recessions
A weighted Bayesian integration method for predicting drug combination using heterogeneous data
Predicting gastrointestinal and renal involvement in adult IgA vasculitis
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Data processing on heterogeneous hardware
ppiPre: predicting protein-protein interactions by combining heterogeneous features
A new computational strategy for predicting essential genes
Predicting disease-related genes using integrated biomedical networks
Zebrafish sex determination and differentiation: Involvement of FTZ-F1 genes
Wilms’ tumor susceptibility: possible involvement of FOXP3 and CXCL12 genes
Predicting cytotoxicity from heterogeneous data sources with Bayesian learning
Measuring and Predicting Heterogeneous Recessions
Measuring and Predicting Heterogeneous Recessions
A weighted Bayesian integration method for predicting drug combination using heterogeneous data
Predicting gastrointestinal and renal involvement in adult IgA vasculitis
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Data processing on heterogeneous hardware
ppiPre: predicting protein-protein interactions by combining heterogeneous features
A new computational strategy for predicting essential genes
Predicting disease-related genes using integrated biomedical networks
Zebrafish sex determination and differentiation: Involvement of FTZ-F1 genes
Wilms’ tumor susceptibility: possible involvement of FOXP3 and CXCL12 genes
Predicting cytotoxicity from heterogeneous data sources with Bayesian learning
Measuring and Predicting Heterogeneous Recessions
Measuring and Predicting Heterogeneous Recessions
A weighted Bayesian integration method for predicting drug combination using heterogeneous data
Predicting gastrointestinal and renal involvement in adult IgA vasculitis
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Data processing on heterogeneous hardware
ppiPre: predicting protein-protein interactions by combining heterogeneous features
A new computational strategy for predicting essential genes
Predicting disease-related genes using integrated biomedical networks
Zebrafish sex determination and differentiation: Involvement of FTZ-F1 genes