Jin Zhou

Mid-career Tenured Researcher, University of California, Los Angeles

1 active project

ZhouLab

Like other forms of big data, biobank data is characterized by its volume, velocity, variety, and veracity (4V). This proposal develops several statistical methods and computational algorithms that address certain aspects of 4V for identifying biomarkers associated with cardio-metabolic related…

Scientific Questions Being Studied

Like other forms of big data, biobank data is characterized by its volume, velocity, variety, and veracity (4V). This proposal develops several statistical methods and computational algorithms that address certain aspects of 4V for identifying biomarkers associated with cardio-metabolic related traits and study their genetic overlap with cognitive functions. We focus on four specific topics. (1) We provide a foundation for developing optimization algorithms for analyzing data that cannot fit into computer memory. (2) Bag of little bootstrap (BLB) for massive variance component models. (3) Variance component selection.

Project Purpose(s)

  • Population Health
  • Drug Development
  • Methods Development
  • Ancestry
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will design and code implementations using the high-performance dynamic language python or Julia if possible. To foster scientific reproducibility and maximize software sustainability, we will embrace modern software engineering practices. We will use observational study design to extract incidence of disease outcomes, e.g., heart failure, stroke, dementia, etc. We will use time-to-event models, e.g., Cox-PH models, to analyze the incidence of diseases.

Anticipated Findings

From our proposal, we expect to develop algorithms, user friendly open-source software, as well as analysis pipelines to encourage efficient and reproducible research. Additionally, from these studies, we also expect that we will identify novel genetic variants or other clinical risk factors implicated in diseases or disease-related traits, a better understanding of how specific genetic variants may impact diseases and traits, how they interact with each other and with lifestyle factors, and how this information could be used to pursue a more personalized approach to medicine. The uniqueness of our proposal is to incorporating time-dependent trajectories into disease predictions and early preventions.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Jin Zhou - Mid-career Tenured Researcher, University of California, Los Angeles

Collaborators:

  • Aubrey Jensen - Project Personnel, University of Arizona
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