- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Hierarchical representation for PPI sites prediction ; volume:23 ; number:1 ; day:20 ; month:3 ; year:2022 ; pages:1-34 ; date:12.2022
BMC bioinformatics ; 23, Heft 1 (20.3.2022), 1-34, 12.2022
- Urheber
-
Quadrini, Michela
Daberdaku, Sebastian
Ferrari, Carlo
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12859-022-04624-y
- URN
-
urn:nbn:de:101:1-2022061109454402208036
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:25 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
- Quadrini, Michela
- Daberdaku, Sebastian
- Ferrari, Carlo
- SpringerLink (Online service)
Ähnliche Objekte (12)
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction
ZiPPi
DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning
Perhimpunan Peladjar Indonesia (PPI)
Improving protein function prediction using domain and protein complexes in PPI networks
Multi-task learning to leverage partially annotated data for PPI interface prediction
Test-Retest-Reliabilität der Präpulsinhibition (PPI) und PPI-Korrelation mit dem Arbeitsgedächtnis
HomPPI: a class of sequence homology based protein-protein interface prediction methods
Towards Learning with Objects in a Hierarchical Representation
Towards learning with objects in a hierarchical representation
Ordnung zur Ausarbeitung des PPI
Ppi geo deog ga jog
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction
ZiPPi
DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning
Perhimpunan Peladjar Indonesia (PPI)
Improving protein function prediction using domain and protein complexes in PPI networks
Multi-task learning to leverage partially annotated data for PPI interface prediction
Test-Retest-Reliabilität der Präpulsinhibition (PPI) und PPI-Korrelation mit dem Arbeitsgedächtnis
HomPPI: a class of sequence homology based protein-protein interface prediction methods
Towards Learning with Objects in a Hierarchical Representation
Towards learning with objects in a hierarchical representation
Ordnung zur Ausarbeitung des PPI
Ppi geo deog ga jog
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction
ZiPPi
DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning
Perhimpunan Peladjar Indonesia (PPI)
Improving protein function prediction using domain and protein complexes in PPI networks
Multi-task learning to leverage partially annotated data for PPI interface prediction
Test-Retest-Reliabilität der Präpulsinhibition (PPI) und PPI-Korrelation mit dem Arbeitsgedächtnis
HomPPI: a class of sequence homology based protein-protein interface prediction methods
Towards Learning with Objects in a Hierarchical Representation
Towards learning with objects in a hierarchical representation
Ordnung zur Ausarbeitung des PPI