Campus Event Calendar
Event Entry
New for: D1, D2, D3, D4, D5
What and Who
Mapping Gene Expression Data onto GO classes
Adrian Alexa
IMPRS
IMPRS Masters' Lunch
AG 1, AG 2, AG 3, AG 4, AG 5
AG Audience
Note: We use this to send email in the morning.
Date, Time and Location
Tuesday, 22 June 2004
13:00
60 Minutes
46.1 - MPII
024
Saarbrücken
Abstract
The analysis of Gene Expression Data from DNA microarray experiments tend
to become an important tool in understanding the mechanism of living
systems. The microarray experiments provide us with a large amount of data
from which is not trivial to understand the significant biological
processes or molecular functions.
Clustering methods based on the numerical expression data have been used
to solve this problem but it seems that their result do not show
biological relevance. New methods have been proposed in which the
Gene-Ontology database is used to gain biological understanding and based
on this some statistics are used to find the most significant genes, and
thus the biological process.
Unfortunately the last methods do not look too deep at the structure of
the GO when computing the statistics. We belief that a more closer look at
the topology of the GO will improve the results. Our goal is to find
methods that use the topology of the GO for a better biological
understanding of the Gene Expression Data.
to become an important tool in understanding the mechanism of living
systems. The microarray experiments provide us with a large amount of data
from which is not trivial to understand the significant biological
processes or molecular functions.
Clustering methods based on the numerical expression data have been used
to solve this problem but it seems that their result do not show
biological relevance. New methods have been proposed in which the
Gene-Ontology database is used to gain biological understanding and based
on this some statistics are used to find the most significant genes, and
thus the biological process.
Unfortunately the last methods do not look too deep at the structure of
the GO when computing the statistics. We belief that a more closer look at
the topology of the GO will improve the results. Our goal is to find
methods that use the topology of the GO for a better biological
understanding of the Gene Expression Data.
Contact
Kerstin Meyer-Ross
0681 - 9325 226
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Christine Kiesel, 06/22/2004 10:12
Christine Kiesel, 06/14/2004 10:57
Christine Kiesel, 06/02/2004 11:12
Christine Kiesel, 05/26/2004 10:09
Christine Kiesel, 05/10/2004 12:50
Christine Kiesel, 05/10/2004 12:48 -- Created document.
Christine Kiesel, 06/14/2004 10:57
Christine Kiesel, 06/02/2004 11:12
Christine Kiesel, 05/26/2004 10:09
Christine Kiesel, 05/10/2004 12:50
Christine Kiesel, 05/10/2004 12:48 -- Created document.