research

Paper by CQM Authors Noted as BMC Systems Biology Editor’s Pick, Highly Accessed

Posted on by Sheryl Rosen
Figure Comparison between the true IRMA network and the networks inferred by our algorithm
The experiment measured the expression level of 5 genes after a shift from galactose-raffinose- to glucose-containing medium. a) The true Yeast Synthetic Network; b) The inferred static network from the Switch ON data set; c) The inferred static network from the Switch OFF data set. Solid gray edges represent inferred interactions that are not present in the real network, or that have the wrong direction (false positives), and dotted gray lines represent false negatives.

A new paper by Dr. M. Paola Vera-Licona and Dr. Reinhard Laubenbacher entitled “An algebra-based method for inferring gene regulatory networks” published in BMC Systems Biology has been noted as an Editor’s pick. It’s also ranked as a highly accessed article. The paper, found in BMC Systems Biology 2014, 8:37, describes a new gene regulatory network inference method based on polynomial dynamical systems incorporates time-resolved gene expression data and existing causal information and into its model inference and uses and evolutionary algorithm for parameter estimation. Congratulations Dr. Vera-Licona and Laubenbacher!  Check out the paper at www.biomedcentral.com/1752-0509/8/37.

Paola Vera-Licona, Abdul Jarrah, Luis David Garcia-Puente, John McGee, Reinhard Laubenbacher. An algebra-based method for inferring gene regulatory networks. BMC Syst Biol. 2014; 8: 37. Published online 2014 March 26. doi: 10.1186/1752-0509-8-37.

Download a full text PDF version of An algebra-based method for inferring gene regulatory networks.

Informatics and Data Analytics Short Course

Posted on by Sheryl Rosen

We are pleased to offer a new Health Informatics Short Course on June 26, 2014 which examines informatics and data analytics for clinical and translational research. We are now accepting registrations from unior and senior researchers, faculty, postdoctoral fellows, graduate students, research assistants and associates, and clinicians who conduct clinical or translational research or who are interested in health informatics. This intensive short course examines the unique characteristics of clinical and life sciences data including the analytic principles, methods and tools for translating health data and information into actionable knowledge for improved health care. The course is presented and cosponsored by the Center for Quantitative Medicine and the CICATS Division of Biomedical Informatics. Learn more at http://cqm.uchc.edu/education/courses/data-analytics-short-course/.