Application of genetic programming in analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer
Poster Oct 29, 2007
Nodal involvement in bladder cancer is an independent indicator of prognosis. This study employed an iterative machine learning process called genetic programming on quantitative expression values of 70 genes to classify primary urothelial carcinoma samples into those associated with or without nodal metastasis. The generated rules showed a strong predilection for ICAM1, MAP2K6 and KDR resulting in gene expression motifs that cumulatively suggested a pattern ICAM1>MAP2K6>KDR for node positive cases.
Quantitative Cell-Based Bioassays for Individual and Combination Immune Checkpoint Immunotherapy TargetsPoster
The human immune system is comprised of a complex network of immune checkpoint receptors that are promising new immunotherapy targets for the treatment of a variety of diseases including cancer and autoimmune-mediated disorders.READ MORE
Applications of chemically modified synthetic guide RNA for CRISPR-Cas9 genome editingPoster
Our results indicate that MS modifications are required for experiments with co-electroporation of Cas9 mRNA and synthetic gRNA, yet have no impact on editing efficiency when delivered with lipid-based transfection reagents.READ MORE
Highly Accurate HCV Genotyping by Targeted Next Generation SequencingPoster
The recent fast advancement of next generation sequencing (NGS) technologies allowing for unprecedented speed and accuracy in analyzing viral genomes are opening new ways to further improve diagnostic genotyping of HCV.