|
|||||
|
|
Missing value imputation improves clustering and interpretation of gene expression microarray data |
|
| Authors: | Johannes Tuikkala Laura L Elo Olli S Nevalainen Tero Aittokallio |
| Institution: | (1) Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland;(2) Department of Mathematics, University of Turku, FI-20014 Turku, Finland;(3) Turku Centre for Biotechnology, PO Box 123, FI-20521 Turku, Finland;(4) Systems Biology Unit, Institut Pasteur, FR-75724 Paris, France |
| Abstract: | BackgroundMissing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used. |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |