research

CQM Researcher Awarded $13M State Contract

Posted on by Sheryl Rosen

Congratulations to Dr. Minakshi Tikoo, on a multi-year, $13 million contract with the Connecticut Department of Social Services in the area of biomedical informatics and health information technology. As part of this award, she and her staff will:

  1. Build an HIT infrastructure for exchange of secure patient information;
  2. Facilitate the development of an HIT Strategic and Operational Plan for the State;
  3. Assist in the implementation of the Medicaid EHR incentive program;
  4. Participate in the Planning and Demonstration Grant for Testing Experience and Functional Tools in Community-Based Long Term Services and Supports (TEFT); and
  5. Serve as the Director of Business Intelligence and Shared Analytics, a new initiative at the Department to exploit “big data”, and the Health and Human Services Health Information Technology Coordinator for the state.

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.