Biomedical Data Science Courses
Study with experts in the field
BIODS 48N: Riding the Data Wave, cross-listed as STATS 48N (Aut)
Instructors: Sabatti, Chiara
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Instructors: Rivas, Manuel; Salzman, Julia; Zou, James
The recent explosion of data generated in the fields of biology and medicine has led to many analytical challenges and opportunities for understanding human health. This graduate-level course focuses on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.
BIODS 220: Artificial Intelligence for Healthcare, crosslisted as CS 271 and BIOMEDIN 220 (Wtr)
Instructors: Yeung, Serena
Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.
BIODS 232: Consulting Workshop on Biomedical Data Science (Aut, Wtr, Spr)
The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (BDS). The educational goal of this workshop is to provide data science consultation training for students. The Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up, helping with immediate action items and summary of the discussion.
Instructors: Lu, Ying; Sabatti, Chiara; Tian, Lu; Desai, Manisha; Efron, Bradley; Lavori, Phil; Narasimhan, Balasubramanian; Tamaresis, John; Tian, Lu
BIODS 237: Deep Learning in Genomics and Biomedicine, crosslisted as CS 273B (Aut)
Instructors: Zou, James
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results.
BIODS 260A/B/C: Workshop in Biostatistics, crosslisted as STATS 260A/B/C (Aut, Wtr, Spr)
Instructors: Sabatti, Chiara; Palacios, Julia (A); Zou, James (B); Rivas, Manuel (C)
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one-page summary of two of the workshops, with choices made by the student.
BIODS 299: Directed Reading and Research (Aut, Wtr, Spr)
Instructors: Bustamante, Carlos; Hastie, Trevor; Olshen, Richard; Rivas, Manuel; Sabatti, Chiara; Salzman, Julia; Tian, Lu; Tibshirani, Rob; Yeung, Serena; Zou, James
For students wishing to receive credit for directed reading or research time.