Pavel P. Kuksa
Alexander Schliep
Pavel P. Kuksa
The data created from high-throughput experimental platforms, such as high-throughput sequencing (HTS), poses computational challenges in particular if advanced statistical approaches such as Bayesian methods are used in analysis. In collaboration with groups at Rutgers, CINJ and CWI we develop methods and analyze HTS data and develop efficient algorithms for Bayesian approaches . At the core of our compressive genomics approach, recently funded by the Big Data to Knowledge Program (BD2K) of the NIH , are r
Single-Cell Genomics (SCG), is the union of high-throughput sequencing (HTS), FACS/microfluidics and WGA, powered by rapidly developing bioinformatics methods. SCG will fundamentally transform studies of microbial biology, biodiversity and phylogeny. speed and effectiveness of the treatment of microbial human pathogens in clinical settings and change our knowledge of microbial biology, bio-diversity and phylogeny. We generated a draft genome of eukaryotes from single-cells , designed fast, cache-efficient k
The methods we develop advance the understanding of gene function by analysis of complex, heterogeneous experimental data including data from imaging, for example from in situ gene expression experiments. Computer vision methods combined with statistical models and semi-supervised learning help to find functional modules in Drosophila development by their spatial and temporal co-expression patterns. Novel tree-models elucidate regulatory mechanisms in the development of the lymphoid system .
Pavel P. Kuksa
Alexander Schliep
Pavel P. Kuksa