Members

Meet some of the talented faculty members, staff, and students in the Quantitative Biomedical Research Center (QBRC) at UT Southwestern Medical Center.

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Labs

In the QBRC, we have multiple research labs for interdisciplinary biological research. Collaboration and teamwork are our keywords here.

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Software

We provide online tools and packages for biological research. Our computational biology team is continually releasing software to aid with statistical analysis.

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Latest News

Using artificial intelligence to personalize lung cancer treatment

January 17, 2023

UTSW research combining artificial intelligence with traditional pathology analysis holds potential for quickly creating a personalized attack plan for cancer patients when speed is essential: as non-small cell lung cancers spread. This approach identified lung cancers that are most likely to respond to one common treatment versus those that might benefit from a different approach.

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Congratulations to Dr. Yang Xie appointed inaugural Associate Deans

May 20, 2022

"I feel honored to serve as the inaugural Associate Dean of Data Sciences because data science is playing increasing roles in all aspects of medical research," Dr. Xie said. "How to analyze, interpret, and utilize this data to understand biology to help with patient care and public health – that has become more and more important."

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Researchers Capture the Big Idea

January 05, 2022

Three UTSW research teams won prizes at the Big Idea Competition, one of the most celebrated nights of entrepreneurship in North Texas held at UT Dallas. Dr. Yang Xie, one of the three finalists, who won $12,500 for creating an algorithm that uses artificial intelligence to improve cancer diagnosis.

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BME 5096-01: Machine Learning

16 weeks starting on Aug 16, 2022

Term: Fall 2022
Days & Times: Tu/Thu, 10:30 am-12:00 pm

  • Introduction to methods for hypothesis testing and statistical inference, and statistical learning methods for prediction and classification.
  • In the first five weeks, the course will cover how to analyze different types of data, including analysis methods for continuous, categorical, and survival. Upon completion of the first ten weeks, students should be able to think critically about data and apply appropriate statistical inference procedures to draw conclusions from such analyses.
  • In the last three weeks, the course will discuss deep learning, Bayesian statistics, causal inference and computational approaches for predictive modeling and data mining.

Course Directors:

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