Posters
« Back
Biomathematical Information Compression and Signal Extraction for Gene Expression Microarray Data Analysis
EP20372
Poster Title: Biomathematical Information Compression and Signal Extraction for Gene Expression Microarray Data Analysis
Submitted on 19 Dec 2013
Author(s): Sofiane Lariani, Elena Comelli, Patrick Descombes and Martin Grigorov
Affiliations: Nestlé Research Center and NCCR, Geneva
Poster Views: 1,383
View poster »


Poster Information
Abstract: Gene expression Microarray data covers both signal (active key genes) and noise. A tentative to recognize any genes expression pattern without clearing the data is like recognizing faces and details on a jammed TV screen: it is hard and we have a high chance to be mistaken. Noise in this context can be related to fluctuation due to the experimental condition, to outliers, and also genes presenting no modulation between groups. In this work we present a biostatisticalmethod for noise filtering and statistical significance assessment.
Summary: Gene expression Microarray data covers both signal (active key genes) and noise. A tentative to recognize any genes expression pattern without clearing the data is like recognizing faces and details on a jammed TV screen. Noise in this context can be related to fluctuation due to the experimental condition, to outliers, and also genes presenting no modulation between groups. In this work we present a biostatisticalmethod for noise filtering and statistical significance assessment. Report abuse »
Creative Commons