Go to content
UR Home

Loss-function learning for digital tissue deconvolution

Görtler, Franziska, Solbrig, Stefan, Wettig, Tilo, Oefner, Peter J. , Spang, Rainer and Altenbuchinger, Michael (2018) Loss-function learning for digital tissue deconvolution. In: Raphael, Benjamin J., (ed.) Research in Computational Molecular Biology : 22nd Annual International Conference, RECOMB 2018, Paris, France, April 21-24, 2018, Proceedings. Lecture Notes in Computer Science, 10812. Springer International Publishing, Cham, pp. 75-89. ISBN 978-3-319-89929-9 ; 978-3-319-89928-2.

Full text not available from this repository.

at arXiv

at publisher (via DOI)

Other URL: https://link.springer.com/book/10.1007%2F978-3-319-89929-9


Abstract

The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution (DTD) addresses the following inverse problem: Given the expression profile $y$ of a tissue, what is the cellular composition $c$ of that tissue? If $X$ is a matrix whose columns are reference profiles of individual cell types, the composition $c$ can be computed by ...

plus


Export bibliographical data



Item type:Book section
Date:25 April 2018
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Identification Number:
ValueType
1801.08447arXiv ID
10.1007/978-3-319-89929-9_5DOI
Related URLs:
URLURL Type
http://arxiv.org/abs/1801.08447v1Preprint
Dewey Decimal Classification:600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:37253
Owner only: item control page
  1. Homepage UR

University Library

Publication Server

Contact:

Publishing: oa@ur.de

Dissertations: dissertationen@ur.de

Research data: daten@ur.de

Contact persons