Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
- Weitere Titel
-
Proteinstrukturvorhersage mit in-cell photo-crosslinking Massenspektrometrie und Deep Learning
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Berlin, Technische Universität Berlin, Dissertation, 2023
- Klassifikation
-
Biowissenschaften, Biologie
- Schlagwort
-
Maschinelles Lernen
Neuronales Netz
Proteine
Strukturaufklärung
Deep Learning
Bioinformatik
- Ereignis
-
Veröffentlichung
- (wo)
-
Berlin
- (wer)
-
Technische Universität Berlin
- (wann)
-
2023
- Urheber
- Beteiligte Personen und Organisationen
- DOI
-
10.14279/depositonce-19025
- Handle
-
11303/20227
- URN
-
urn:nbn:de:101:1-2023110800585727894958
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:53 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
- Stahl, Kolja
- Brock, Oliver
- Rappsilber, Juri
- Karaca, Ezgi
- Cheng, Jianlin
- Technische Universität Berlin
Entstanden
- 2023
Ähnliche Objekte (12)
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Modelling protein complexes with crosslinking mass spectrometry and deep learning
Quantitative photo-crosslinking mass spectrometry revealing protein structure response to environmental changes
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
An integrated workflow for crosslinking mass spectrometry
Structure-based prediction and characterization of photo-crosslinking in native protein–RNA complexes
A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data
Selective cross‐linking of coinciding protein assemblies by in‐gel cross‐linking mass spectrometry
Data analysis strategies for proteome-wide crosslinking mass spectrometry
Data-independent acquisition improves quantitative cross-linking mass spectrometry
Investigating chemical crosslinking of protein-RNA complexes by mass spectrometry
Reliable identification of protein-protein interactions by crosslinking mass spectrometry
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Modelling protein complexes with crosslinking mass spectrometry and deep learning
Quantitative photo-crosslinking mass spectrometry revealing protein structure response to environmental changes
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
An integrated workflow for crosslinking mass spectrometry
Structure-based prediction and characterization of photo-crosslinking in native protein–RNA complexes
A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data
Selective cross‐linking of coinciding protein assemblies by in‐gel cross‐linking mass spectrometry
Data analysis strategies for proteome-wide crosslinking mass spectrometry
Data-independent acquisition improves quantitative cross-linking mass spectrometry
Investigating chemical crosslinking of protein-RNA complexes by mass spectrometry
Reliable identification of protein-protein interactions by crosslinking mass spectrometry
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Modelling protein complexes with crosslinking mass spectrometry and deep learning
Quantitative photo-crosslinking mass spectrometry revealing protein structure response to environmental changes
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
An integrated workflow for crosslinking mass spectrometry
Structure-based prediction and characterization of photo-crosslinking in native protein–RNA complexes
A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data
Selective cross‐linking of coinciding protein assemblies by in‐gel cross‐linking mass spectrometry
Data analysis strategies for proteome-wide crosslinking mass spectrometry
Data-independent acquisition improves quantitative cross-linking mass spectrometry
Investigating chemical crosslinking of protein-RNA complexes by mass spectrometry