Showing 481 - 495 of 20642

The research goal of this project is to develop a biologically informed machine learning (ML) model that integrates datasets from different studies, and leverages current biological knowledge in an automated manner, to improve predictions in biological data analysis.

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The research goal of this project is to construct and streamline an approach to identify phenotype-relevant signatures by integrating various proteomics data. Leveraging protein structures and interaction networks, we will map structural changes and post-translational modifications to identify...

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The research goal of this project is to develop new theory and tools that leverage evolutionary perspectives and knowledge of the energetics of reactions to predict the most likely regulation in a given environment. These methods will accelerate exploration, modeling and understanding of cell...

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    3

By developing explainable, predictive metabolic models of individual microbes, we aim to design consortia that convert light and abundant atmospheric gases into high-value molecules through microbial division of labor.

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The research goal of this project is to develop computational methods to predict cell regulation phenotypes using small molecule and proteome data to understand outcomes in complex biological systems.

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The research goal of this project is to identify and control host functions hijacked during viral infection through use of PNNL ‘omics technologies and modeling capabilities.

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    5

Jeremy Zucker is a computational scientist in the department of computational biology at Pacific Northwest National Laboratory. He is a principal investigator for a variety of sponsors, including DARPA, DOE, and internally funded LDRDs. He uses machine learning-powered causal models — abstract and...

Ethan is an applied mathematician with experience in control, optimization, modeling, and machine learning. He is interested in leveraging the tools and successes of data science to push the boundaries of complexity and scale possible within scientific computing. Within modeling, his interests are...

Most of Vlad's research is focused on age-related neurodegenerative disorders and fundamentals of aging and the molecular mechanisms underpinning those phenomena. He considers mass spectrometry-based proteomics as a key tool in his research. The richness of the mass spectrometry data and complexity...

Margaret S. Cheung is a biological physicist and a computational scientist on the Computing, Analytics, and Modeling team at EMSL. She graduated from the National Taiwan University in 1994 and went on to obtain a Ph.D. degree from the University of California at San Diego in 2003. She was then...

In general, I am interested in studying complex systems by integrating both mechanism-based and data-driven approaches, in order to understand their dynamics, evolution, control, and design. The ultimate goal is to understand (some of) the design principles of complex systems, whether it is physical...

Samantha Powell earned her PhD from the University of Oklahoma in the lab of Dr. George Richter-Addo, using X-ray crystallography to study heme proteins and Clostridium difficile nitroreductases and their interactions with small molecules. From 2019-2020, she was a National Research Council...

The science objective of this project is to apply structural proteomics technologies to map the molecular interactome.

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The research objective of this project is to develop an integrative and automated multi-PTM profiling capability with deep proteome coverage.

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    2

The research goal of this project is to use stimuli-specific, synthetic nanobodies to target functional mediators without prior knowledge of the response networks or manipulating the biological system.

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