Recent technological advances enable biomedical investigators to observe the gene of entire organisms in action by simultaneously measuring the level of activation of thousands of genes under the same experimental conditions. This technology, known as microarrays, provides today unprecedented discovery opportunities and it is reshaping biomedical sciences.
Preliminary Abstract due
May 15th 2002
Parallel to these technological advances has been the development of machine learning methods able to integrate and understand the data generated by this new kind of experiments. However, most of this research has been conducted outside the traditional machine learning research community. The aim of this special issue is to bridge this divide by reporting methodological advances in automated learning from functional genomic data to the core machine learning community. The special issue seeks significant methodological contributions of proven or potential impact on functional genomics.
Topics of particular interest include, but are not limited to:
 |
Hybridization detection, signal amplification and noise control in microarray experiments. |
|
Differential analysis and classification of gene expression data. |
|
Clustering and other unsupervised approaches to class discovery in functional genomics. |
|
Temporal profiling and analysis of dynamic genomic systems. |
|
Dependency discovery and reverse engineering of genetic networks. |
|
Validation methods to assess reliability and reproducibility of experiments. |
Articles accepted for the special issue will be permanently posted on the WWW and will constitute the core of a free portal to AI and machine learning resources in bioinformatics (genomethods.org).
