Prediction of gene expression with cis-SNPs using mixed models and regularization methods
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2164
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Prediction of gene expression with cis-SNPs using mixed models and regularization methods ; volume:18 ; number:1 ; day:11 ; month:5 ; year:2017 ; pages:1-11 ; date:12.2017
BMC genomics ; 18, Heft 1 (11.5.2017), 1-11, 12.2017
- Urheber
-
Zeng, Ping
- Beteiligte Personen und Organisationen
-
Zhou, Xiang
Huang, Shuiping
SpringerLink (Online service)
- DOI
-
10.1186/s12864-017-3759-6
- URN
-
urn:nbn:de:1111-201705213139
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:56 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zeng, Ping
- Zhou, Xiang
- Huang, Shuiping
- SpringerLink (Online service)
Ähnliche Objekte (12)
Superlubricitive engineering—Future industry nearly getting rid of wear and frictional energy consumption
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies
SRT-Server: powering the analysis of spatial transcriptomic data
N4 DNA recognition by STAT6: structural and functional implications
Spatially aware dimension reduction for spatial transcriptomics
BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
Demystifying “drop-outs” in single-cell UMI data
Only the new beginning of VAP quality control
Lactate and stepwise lactate kinetics can be used to guide resuscitation
Cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors in diabetic and nondiabetic patients
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
How, and For Whom, Does Higher Education Increase Voting?
Superlubricitive engineering—Future industry nearly getting rid of wear and frictional energy consumption
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies
SRT-Server: powering the analysis of spatial transcriptomic data
N4 DNA recognition by STAT6: structural and functional implications
Spatially aware dimension reduction for spatial transcriptomics
BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
Demystifying “drop-outs” in single-cell UMI data
Only the new beginning of VAP quality control
Lactate and stepwise lactate kinetics can be used to guide resuscitation
Cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors in diabetic and nondiabetic patients
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
How, and For Whom, Does Higher Education Increase Voting?
Superlubricitive engineering—Future industry nearly getting rid of wear and frictional energy consumption
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies
SRT-Server: powering the analysis of spatial transcriptomic data
N4 DNA recognition by STAT6: structural and functional implications
Spatially aware dimension reduction for spatial transcriptomics
BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
Demystifying “drop-outs” in single-cell UMI data
Only the new beginning of VAP quality control
Lactate and stepwise lactate kinetics can be used to guide resuscitation
Cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors in diabetic and nondiabetic patients
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies