Dr. Noh's research specializes in deducing causal inference for time series microscopy images and developing statistical methods for genomic data analysis. Currently, he collaborates with peers in the Green Center for Systems Biology and in the Lyda Hill Department of Bioinformatics to develop statistical methods to resolve complex biomedical data.
We seek to understand how cancer cells harness the cytoskeleton to promote tumor growth, drug resistance and cancer metastasis through non-genetic, morphologic signaling programs.
Our aim is to develop computational methods to unveil the hidden biological circuitries behind the data, from understanding sequence-based regulations to the evolution of genomes and their impact to diseases.
Our lab combines normative theories and biologically plausible neural circuit models to study the principles of neural information processing, in order to answer how perception, cognition, and behavior emerge from neural circuits.
I am interested in developing computational models and algorithms for big data to predict patients' outcomes, which can help clinicians to tailor treatment plans for individual patients.
Our team is interested in developing computational models to predict patient outcomes, which will allow clinicians to tailor treatment plans for individual patients.
The Saunders Lab aims to advance our understanding of the bacterial domain of life using high throughput genetics to map the molecular interactions that underly cellular physiology.
We are driven by the belief that the spatial organization of tissue provides a powerful window into cell-cell interactions, a crucial component of disease progression and response.
We explore questions on genomes using a systems biology approach: developing and employing integrative approaches at the interface of gene regulation, epigenetics, single-cell genomics, and bioinformatics.
The Dean Lab aims to develop and apply cutting-edge microscopy instrumentation and analyses to gain insight into otherwise intractable biological problems.
