2021 Winter: BIODS 215

Welcome to BIODS215 Topics in Biomedical Data Science: Large-scale inference!

• Website for 2020 Winter quarter is available here
• Website for 2018 Winter quarter is available here
• Website for 2017 Spring quarter is available here
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Teaching team

We highly encourage everyone to use Piazza to contact with the teaching team. However, you can also reach out to me using email. E-mail me at mrivas at stanford.edu .

Instructors

• Manuel A. Rivas (email: mrivas at stanford.edu), Office hours: Tuesdays 3:00 - 4:00 (Zoom link will be shared to the class via Axess)

Links

• Canvas

Course content plan

Assignments

Please submit all the assignments via Canvas.

Late day policy. Students have 6 late days in total. We allow a maximum of 2 days per assignment.

Reading Materials

We ask students to write a paragraph about the reading materials and/or the corresponding lecture. Here is the instructions:

• Please read the reading materials posted on the class website and write a paragraph about it. You may write more if you want.
• You can write anything regarding the reading material and/or the corresponding lecture.
• You may also include comments and/or request regarding the teaching style. We appreciate your feedback!
• Please submit your answer through Canvas as one pdf document.

Problem set 1

Will be updated tonight.

Class project

We think the class project is a great opportunity for you to use some of the methods you will learn in the class. To allocate sufficient time to work on the project, we would like to have the brief project proposal by the third week of the quarter 1/26/2021.

Lecture materials

We will post the list of lecture slides and reading materials here.

Lecture 1. Overview of emerging topics in biomedical data science

• Lecture slides (on Canvas)
• D. Donoho. 50 years of Data Science
• Chapter 18 and Epilogue of Computer Age Statistical Inference

Lecture 2. Mixture models

• Lecture slides (on Canvas)
• Splice Disruption Mixture Model
• Sex Effects Mixture Model

Lecture 4. Causal inference using instrumental variables

• Burgess, S., Foley, C. N. & Zuber, V. Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annual Review of Genomics and Human Genetics 19, 303–327 (2018). (please clink “View on Journal Site”)
• Hernan, M. A. & Robins, J. M. Instruments for Causal Inference: An Epidemiologist??s Dream? Epidemiology 17, 360–372 (2006).
• P Cannon, C. IMPROVE-IT Trial: A Comparison of Ezetimibe/Simvastatin versus Simvastatin Monotherapy on Cardiovascular Outcomes After Acute Coronary Syndromes. in (2014).
• Burgess, S., Foley, C. N. & Zuber, V. Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annual Review of Genomics and Human Genetics 19, 303–327 (2018).

Lecture 5. Causal inference using instrumental variables II

Amar, David, et al. “Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.” Nature Communications 12.1 (2021): 1-11.

Lecture 6. LassoNet (guest lecture)

• Lemhadri, I., Ruan, F. & Tibshirani, R. A neural network with feature sparsity. arXiv:1907.12207 [cs, stat] (2019).