ICoHR Lab

Integrative Computational Health Research Lab

The Integrative Computational Health Research (ICoHR) Lab is an interdisciplinary research effort in the School of Health and Medical Professions, University of Idaho.

Goals

  • Utilize multi-dimensional large-scale datasets that span across clinical, social, and genomic aspects of health to improve health outcomes at a population level
  • Promote data literacy and computational skills in undergraduate, graduate, and health professional students
  • Advance nursing science in omics research and precision health with a holistic approach for rural populations

Students

I welcome students who are interested in data science for health research or quantitative genomics. While programming experience and a statistics background are helpful, they are not required as long as you are motivated to learn.

I do not have funded positions at this time. But if you are interested in joining one of the projects below, or in developing a related project of your own, I would like to hear from you — students gain computational research experience and co-authorship on conference abstracts or manuscripts. Please email me at yesols@uidaho.edu with a brief summary of your background, interests, and goals.

Projects

Polysocial risk score for psychiatric conditions

Health risk is shaped by where people live and work, the stresses they carry, and the resources they can reach. This project combines many social and clinical factors into a single score, tests how well it predicts psychiatric conditions, and asks whether social circumstances change the effect of inherited genetic risk.

  • PI: Colin Xu, Department of Psychology and Communication, CLASS
  • Develop machine-learning-based predictive tools from clinical and social data to predict depression, anxiety, PTSD, and substance use disorders
  • Investigate interaction between genetic risk and social risk

Long-term health outcomes of cancer survivors

More people survive cancer than ever before, but many carry health problems for years after treatment ends, and those burdens are not distributed evenly. In this project, we ask which clinical, social, and genetic factors explain why some survivors do well over the long term and others do not.