Identifying common transcriptome signatures of cancer by interpreting deep learning models
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1474-760X
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Identifying common transcriptome signatures of cancer by interpreting deep learning models ; volume:23 ; number:1 ; day:17 ; month:5 ; year:2022 ; pages:1-23 ; date:12.2022
Genome biology ; 23, Heft 1 (17.5.2022), 1-23, 12.2022
- Creator
-
Jha, Anupama
Quesnel-Vallières, Mathieu
Wang, David
Thomas-Tikhonenko, Andrei
Lynch, Kristen W.
Barash, Yoseph
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s13059-022-02681-3
- URN
-
urn:nbn:de:101:1-2022072307074779906250
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:22 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
- Jha, Anupama
- Quesnel-Vallières, Mathieu
- Wang, David
- Thomas-Tikhonenko, Andrei
- Lynch, Kristen W.
- Barash, Yoseph
- SpringerLink (Online service)
Other Objects (12)
Twiner: correlation-based regularization for identifying common cancer gene signatures
Identifying key signatures of highly productive CHO cells from transcriptome and proteome profiles
Transcriptome signatures associated with meningioma progression
Automating airborne pollen classification: identifying and interpreting hard samples for classifiers
Reference-free transcriptome signatures for prostate cancer prognosis
Prognostic genome and transcriptome signatures in colorectal cancers
Interpreting models interpreting brain dynamics
From emergence to maintenance: A model for identifying and interpreting strategic Issues
A Bayesian approach to identifying and interpreting regional convergence clubs in Europe
OSCE Insights 2021 : Identifying Common Ground
OSCE Insights 2021 : Identifying Common Ground
Specific age related signatures in Drosophila body parts transcriptome
Twiner: correlation-based regularization for identifying common cancer gene signatures
Identifying key signatures of highly productive CHO cells from transcriptome and proteome profiles
Transcriptome signatures associated with meningioma progression
Automating airborne pollen classification: identifying and interpreting hard samples for classifiers
Reference-free transcriptome signatures for prostate cancer prognosis
Prognostic genome and transcriptome signatures in colorectal cancers
Interpreting models interpreting brain dynamics
From emergence to maintenance: A model for identifying and interpreting strategic Issues
A Bayesian approach to identifying and interpreting regional convergence clubs in Europe
OSCE Insights 2021 : Identifying Common Ground
OSCE Insights 2021 : Identifying Common Ground
Specific age related signatures in Drosophila body parts transcriptome
Twiner: correlation-based regularization for identifying common cancer gene signatures
Identifying key signatures of highly productive CHO cells from transcriptome and proteome profiles
Transcriptome signatures associated with meningioma progression
Automating airborne pollen classification: identifying and interpreting hard samples for classifiers
Reference-free transcriptome signatures for prostate cancer prognosis
Prognostic genome and transcriptome signatures in colorectal cancers
Interpreting models interpreting brain dynamics
From emergence to maintenance: A model for identifying and interpreting strategic Issues
A Bayesian approach to identifying and interpreting regional convergence clubs in Europe
OSCE Insights 2021 : Identifying Common Ground
OSCE Insights 2021 : Identifying Common Ground