Sat, 19 Jul 2008 01:18:11 BST CiteULike: nedwards's Qin http://www.citeulike.org/user/nedwards/author/Qin CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/nedwards/article/835519 <i>Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.</i><br /><br />We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function. Coexpression analysis of human genes across many microarray data sets. HK Lee AK Hsu J Sajdak J Qin P Pavlidis doi:10.1101/gr.1910904 Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094. 2006-09-08T15:43:38-00:00 2004 Genome Res 1088-9051 14 6 1085 1094 data-integration gene-expression systems-biology http://www.citeulike.org/user/nedwards/article/864592 <i>Mol Cell Proteomics, Vol. 5, No. 3. (March 2006), pp. 497-509.</i><br /><br />Manual analysis of mass spectrometry data is a current bottleneck in high throughput proteomics. In particular, the need to manually validate the results of mass spectrometry database searching algorithms can be prohibitively time-consuming. Development of software tools that attempt to quantify the confidence in the assignment of a protein or peptide identity to a mass spectrum is an area of active interest. We sought to extend work in this area by investigating the potential of recent machine learning algorithms to improve the accuracy of these approaches and as a flexible framework for accommodating new data features. Specifically we demonstrated the ability of boosting and random forest approaches to improve the discrimination of true hits from false positive identifications in the results of mass spectrometry database search engines compared with thresholding and other machine learning approaches. We accommodated additional attributes obtainable from database search results, including a factor addressing proton mobility. Performance was evaluated using publically available electrospray data and a new collection of MALDI data generated from purified human reference proteins. Improved classification of mass spectrometry database search results using newer machine learning approaches. PJ Ulintz J Zhu ZS Qin PC Andrews doi:10.1074/mcp.M500233-MCP200 Mol Cell Proteomics, Vol. 5, No. 3. (March 2006), pp. 497-509. 2006-09-23T20:19:02-00:00 2006 Mol Cell Proteomics 1535-9476 5 3 497 509 machine-learning peptide-identification peptide-identification-statistics proteomics tandem-mass-spectrometry