Progress in understanding of molecular mechanisms underlying complex heritable disorders (e.g., autism, schizophrenia, diabetes) depends on new bioinformatics approaches for systems-level analysis and identification of disease-specific patterns of inheritance.
Computational platform LYNX (http://lynx.ci.uchicago.edu:8080/) supports the analysis of common heritable disorders from the systems biology perspective.
Our approach is based on a large-scale integration of genomic and clinical data and various classes of biological information from over 35 public and private databases. This data is used for the identification of genes and molecular networks contributing to phenotypes of interest, as well as for the prediction of additional high-confidence disease genes to be tested experimentally.
Lynx Single-Gene Page provides annotations for a sigle gene of interest from a wide variety of databases integreated in our Lynx KB.
For e.g: AKT1 , PXN
Lynx Multi-Gene Page provides an interface for a list of genes.
Our analytical strategy is three-fold and includes:
Networks-based gene prioritization leverages previous work of our collaborator Dr. Börnigen-Nitsch  and utilizes Heat Kernel diffusion, Random Walk, PageRank with priors, HITS with priors and K-step Markov model algorithms. These algorithms were modified to accommodate a variety of weighted data types to be used for gene prioritization.
Our approach is being used for analysis of genomics data for a number of translational medicine projects at the University of Washington, UCLA, UCSC, Cornell University and the University of Rochester.
 Nitsch D, Tranchevent LC, Goncalves JP, Vogt JK, Madeira SC, Moreau Y. PINTA: a web server for network-based gene prioritization from expression data. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W334-8. doi: 10.1093/nar/gkr289. Epub 2011 May 20. PubMed PMID: 21602267; PubMed Central PMCID: PMC3125740.