Output details
11 - Computer Science and Informatics
University of Bristol
Article title
Bayesian Unsupervised Learning with Multiple Data Types
Type
D - Journal article
Title of journal
Statistical Applications in Genetics and Molecular Biology
Article number
Article 27
Volume number
8
Issue number
-
First page of article
-
ISSN of journal
1544-6115
Year of publication
2009
Number of additional authors
2
Additional information
<24> This paper was one of the first attempts to propose Bayesian unsupervised learning methods which use more than one type of data. This area is now important in the analysis of medical data sets, where multiple types of data are derived from the same patient. The paper resulted in invitations to speak at the Institute for Cancer Research, a workshop at the Royal Society (March 2011)and at the Breakthrough Breast Cancer Research Unit (June 2011). Some predications of the paper were later investigated in experimental work using cell lines donated by the Institute for Cancer Research.
Interdisciplinary
-
Cross-referral requested
-
Research group
B - Intelligent Systems Laboratory
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
