00230212: Intelligent Bio-computation (智能生物计算) (Fall 2024)

Course Information

When: Monday 19:20 pm – 20:55 pm.
Where: 清华大学法律图书馆B112.
Instructor: Jianzhu Ma (马剑竹) Email: majianzhu at tsinghua dot edu dot cn.
Office Hour: Tuesday. 19:30 pm – 20:30 pm.or by appointment (send e-mail) Where: TBD

Course description:

This course encompasses all the principal areas within bioinformatics and computational biology, making it an 'all-you-need’ program for those aspiring to craft algorithms for analyzing and interpreting biological data. Students who complete this course will gain insights into a range of fundamental algorithmic strategies, drawn directly from seminal and highly referenced recent literature. The course imparts foundational knowledge in statistics, mathematics, and machine learning, including deep learning aspects. Both lectures and student-led presentations are provided, as detailed in the Syllabus. The curriculum advances beyond mere application of existing software; it endeavors to impart a thorough understanding of the core principles behind today's cutting-edge techniques, equipping students with the skills to devise and pioneer the forthcoming wave of bioinformatics tools.

Prerequisites:

This course is intended to be a first course in Bioinformatics for both undergraduate and graduate students in Electronic Engineering, Computer Science, Mathematics, Statistics, and Life Sciences. Students from non-biological disciplines may wonder about the level of prior biological knowledge required to pursue studies in bioinformatics. The answer is minimal; no advanced understanding beyond what is typically taught in high school biology is necessary. But you will need a willingness to learn biological concepts needed to understand the computational problems we study. We will begin with a quick review of some of the biological concepts needed for bioinformatics, and will learn more as needed.
Students are expected to have the following background:

• Basic programming skills to write a reasonably non-trivial computer program in Python.

• At least an undergraduate course in algorithms (a graduate course in algorithms is preferred).

• Undergraduate or graduate level machine learning courses.

• Students without this background should discuss their preparation with the instructor. The introduction session in the first week of the class will give an overview of the expected background.

Optional textbooks

“Biological sequence analysis” by Durbin, Eddy, Krogh, Mitchison
“An introduction to bioinformatics algorithms” by Jones, Pevzner
“Network Biology:” section in Topology of molecular interaction networks
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Online book “Bioinformatics Algorithms” [link], Phillip Compeau and Pavel Pevzner

Grading

• Homework (50%)

• Final projects (50%)

Assignments

There will be FOUR homework assignments including both theory and programming problems. Programming projects can be written in both R and Python. Deep learning final projects should be written in either Tensorflow and PyTorch.

Late policy

Assignments are to be submitted by the due date listed. Assignments will NOT BE accepted if they are submitted late. Additional extensions will be granted only due to serious and documented medical or family emergencies.

Final projects

Students are encouraged to work as teams of two or three. Each team can either choose the provided projects on the course website or a separate project related to Biology. If you choose to do your own project, please contact me beforehand to discuss it. Here are the bioinformatics projects you might consider:

• Easy projects:

  • Using deep neural networks to predict protein secondary structures.

  • Using deep neural networks to predict protein contact/distance map.

  • Implement a RNA secondary structure prediction algorithm.

  • Implement a single cell analysis pipeline.

  • Predicting protein functions prediction using multiple protein interaction networks.

  • Implement an algorithm to correct the batch effect in single cell data.

  • Predicting enhancers using genomics features.

• Advanced projects:

  • Compression of protein multiple sequence alignment files.

  • Implement a (hierarchical) network community detection algorithms to recover Gene Ontology.

  • Protein function prediction from interaction network using graph.

Syllabus (tentative)

Sequence Analysis (3 classes)

Introduction to Machine Learning (2 classes)

Structure Biology (3 classes)

AI Drug Development (1 classes)

Single Cell in Bioinformatics (2 classes)

Network in Bioinformatics (2 classes)

Project presentations (1 classes)