@article {552, title = {Functional genomics and networks: new approaches in the extraction of complex gene modules.}, journal = {Expert Rev Proteomics}, volume = {7}, year = {2010}, month = {2010 Feb}, pages = {55-63}, abstract = {

The engine that makes the cell work is made of an intricate network of molecular interactions. Nowadays, the elements and relationships of this complex network can be studied with several types of high-throughput techniques. The dream of having a global picture of the cell from different perspectives that can jointly explain cell behavior is, at least technically, feasible. However, this task can only be accomplished by filling the gap between data and information. The availability of methods capable of accurately managing, integrating and analyzing the results from these experiments is crucial for this purpose. Here, we review the new challenges raised by the availability of different genomic data, as well as the new proposals presented to cope with the increasing data complexity. Special emphasis is given to approaches that explore the transcriptome trying to describe the modules of genes that account for the traits studied.

}, keywords = {Gene Expression Regulation, Gene Regulatory Networks, Genomics, Protein Binding, Proteins, Systems biology}, issn = {1744-8387}, doi = {10.1586/epr.09.103}, author = {Minguez, Pablo and Dopazo, Joaquin} } @article {592, title = {Functional profiling of microarray experiments using text-mining derived bioentities.}, journal = {Bioinformatics}, volume = {23}, year = {2007}, month = {2007 Nov 15}, pages = {3098-9}, abstract = {

MOTIVATION: The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.

}, keywords = {Artificial Intelligence, Databases, Protein, Gene Expression Profiling, Information Storage and Retrieval, Natural Language Processing, Proteins, Research Design, Systems Integration}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm445}, author = {Minguez, Pablo and Al-Shahrour, F{\'a}tima and Montaner, David and Dopazo, Joaquin} }