Researchers at UT Southwestern Medical Center have developed a novel artificial intelligence (AI) model that analyzes the spatial arrangement of cells in tissue samples. This innovative approach, detailed in Nature Communications, accurately predicted outcomes for cancer patients, marking a significant advancement in utilizing AI for cancer prognosis and personalized treatment strategies.Read more
Meet some of the talented faculty members, staff, and students in the Quantitative Biomedical Research Center (QBRC) at UT Southwestern Medical Center.Learn more
In the QBRC, we have multiple research labs for interdisciplinary biological research. Collaboration and teamwork are our keywords here.Learn more
We provide online tools and packages for biological research. Our computational biology team is continually releasing software to aid with statistical analysis.Learn more
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.Read more
"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."Read more
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.Read more
Dr. Yang Xie, Director of UT Southwestern’s Quantitative Biomedical Research Center (QBRC), made headlines this summer when she developed a tool to predict which patients would benefit the most from aggressive high blood pressure treatment.Read more
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.