Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  EpiGRAPHregression: A toolkit for (epi-)genomic correlation analysis and prediction of quantitative attributes

Halachev, K. (2006). EpiGRAPHregression: A toolkit for (epi-)genomic correlation analysis and prediction of quantitative attributes. Master Thesis, Universität des Saarlandes, Saarbrücken.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-22BA-B Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0027-D52C-E
Genre: Hochschulschrift

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Halachev_MasterThesis_EpiGRAPHregression.pdf (beliebiger Volltext), 2MB
 
Datei-Permalink:
-
Name:
Halachev_MasterThesis_EpiGRAPHregression.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Eingeschränkt (Max Planck Institute for Informatics, MSIN; )
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Halachev, Konstantin1, 2, Autor           
Lengauer, Thomas1, Ratgeber           
Bock, Christoph1, Ratgeber           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Five years ago, the human genome sequence was published, an important milestone towards understanding human biology. However, basic cell processes cannot be explained by the genome sequence alone. Instead, further layers of control such as the epigenome will be important for significant advances towards better understanding of normal and disease-related phenotypes. A new research field in computational biology is currently emerging that is concerned with the analysis of functional information beyond the human genome sequence. Our goal is to provide biologists with means to navigate the large amounts of epigenetic data and with tools to screen these data for biologically interesting associations. We developed a statistical learning methodology that facilitates mapping of epigenetic data against the human genome, identifies areas of over- and underrepresentation, and finds significant correlations with DNA-related attributes. We implemented this methodology in a software toolkit called EpiGRAPHregression. EpiGRAPHregression is a prototype of a genome analysis tool that enables the user to analyze relationships between many attributes, and it provides a quick test whether a newly analyzed attribute can be efficiently predicted from already known attributes. Thereby, EpiGRAPHregression may significantly speed up the analysis of new types of genomic and epigenomic data.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 20062006
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: Saarbrücken : Universität des Saarlandes
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 314491
Anderer: Local-ID: C125673F004B2D7B-56DA5A90E26AE559C12572350051361B-HalachevMaster2006
 Art des Abschluß: Master

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle

einblenden: