Name
Title | ||
|---|---|---|
A genome wide RNAI screen by time lapse microscopy in order to identify mitotic genes — computational aspects and challenges |
Abstract | ||
|---|---|---|
The MitoCheck project aims at identifying and characterizing the function of genes involved in mitosis in live human cells. The genome wide RNA interference screen developed for this purpose is based on automatic time-lapse microscopy to analyze the phenotypes after knocking down each human gene individually in cultured cells whose chromosomes are fluorescently labeled. Such a screen produces large amounts of digital image data (~ 200.000 video sequences, i.e. over 18 millions of images) which can no longer be handled and interpreted manually. We have developed an image processing method consisting of segmentation, feature extraction and automatic classification, which assigns to each nucleus in each image one out of several predefined morphological classes. Using the relative cell counts in each of these classes, measured over time for each experiment, we derive a phenotypic fingerprint for each gene that allows clustering of genes by functional similarity. This paper will give an overview over the computational aspects of this screen. The complete quality controlled data set and phenotypic measurements will be available after publication on http://www.mitocheck.org/. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISBI.2008.4540999 | Paris |
Keywords | Field | DocType |
cellular biophysics,feature extraction,genetics,image classification,image segmentation,macromolecules,medical image processing,organic compounds,MitoCheck project,chromosomes,feature extraction,fluorescence,genome wide RNAI screen,image processing method,image segmentation,live human cells,mitotic genes,phenotypic fingerprint,time lapse microscopy,Biomedical Image Processing,Genome-wide RNAi Screen,Mitosis,Pattern Recognition | Genome,Computer vision,Pattern recognition,Segmentation,Computer science,Image processing,Feature extraction,Image segmentation,Time-lapse microscopy,Artificial intelligence,Contextual image classification,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4244-2003-2 | 0 |
PageRank | References | Authors |
0.34 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Walter | 1 | 0 | 0.34 |
| Michael Held | 2 | 911 | 509.89 |
| Beate Neumann | 3 | 0 | 0.34 |
| Jean-Karim Hériché | 4 | 128 | 15.09 |
| Christian Conrad | 5 | 25 | 6.03 |
| Rainer Pepperkok | 6 | 19 | 3.31 |
| Jan Ellenberg | 7 | 46 | 7.40 |