Molecular networks in microarray analysis. Export

@article{citeulike:2184292, abstract = {Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.}, address = {Ariadne Genomics, Inc., 9430 Key West avenue, Suite 113, Rockville, MD 20850, USA. sivachenko@ariadnegenomics.com}, author = {Sivachenko, Andrey Y. and Yuryev, Anton and Daraselia, Nikolai and Mazo, Ilya}, citeulike-article-id = {2184292}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/17636854}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=17636854}, issn = {0219-7200}, journal = {Journal of bioinformatics and computational biology}, keywords = {analysis, methods, microarray, network, pathway}, month = {April}, number = {2B}, pages = {429--456}, posted-at = {2009-10-11 01:58:07}, priority = {2}, title = {Molecular networks in microarray analysis.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17636854}, volume = {5}, year = {2007} }

TY - JOUR ID - citeulike:2184292 L3 - citeulike-article-id:2184292 N2 - Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed. IS - 2B JF - Journal of bioinformatics and computational biology SN - 0219-7200 AD - Ariadne Genomics, Inc., 9430 Key West avenue, Suite 113, Rockville, MD 20850, USA. sivachenko@ariadnegenomics.com EP - 456 TI - Molecular networks in microarray analysis. VL - 5 SP - 429 KW - analysis KW - methods KW - microarray KW - network KW - pathway AU - Sivachenko, Andrey AU - Yuryev, Anton AU - Daraselia, Nikolai AU - Mazo, Ilya PY - 2007/04// UR - http://view.ncbi.nlm.nih.gov/pubmed/17636854 ER -