Maria Chikina, Ph.D.
|Ph.D. in Molecular Biology, Princeton University|
3501 Fifth Avenue
The rise of genome-scale experimental methods has greatly accelerated the speed of biological data accumulation. However, as datasets increase in size, it becomes easier to find patterns and correlations, but harder to distinguish true biological insight from technological and statistical artifacts. Consequently, exploiting large-scale datasets to inform our understanding of biological systems remains a challenge. My work has focused on bridging the gap between statistically rigorous computational techniques and knowledge of underlying biological and experimental processes to develop methods that overcome the biases and artifacts inherent in the structure of large-scale datasets and transform noisy data into concrete biological knowledge.
Overacre-Delgoffe AE, Chikina M, Dadey RE, Yano H, Brunazzi EA, Shayan G, Horne W, Moskovitz JM, Kolls JK, Sander C, Shuai Y, Normolle DP, Kirkwood JM, Ferris RL, Delgoffe GM, Bruno TC, Workman CJ, Vignali DAA. (2017) Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity. Cell [Epub ahead of print]
Chikina M, Frieze A, Pegden W (2017) Assessing significance in a Markov chain without mixing. Proc Natl Acad Sci U S A. 114(11):2860-2864.
Chikina M, Robinson JD, Clark NL (2016) Hundreds of Genes Experienced Convergent Shifts in Selective Pressure in Marine Mammals Mol Biol Evol. Jun 21. pii: msw112. [Epub ahead of print].