Programming for Biomedical Informatics
Informatics (INFR11260), Semester 1, 2025
All course materials can be kept up to date by syncing with the course GitHub repository
Week 8 Tasks
keep practicing with your preferred python coding environment using the course notebooks.
have a go at the new week 8 practice assignment in the GitHub Classroom, this one is to practice some consenus clustering. Once again I’ve decided to release my solution along with the rest of this week’s materials for this so you can look at this if you would like to.
On Tuesday, we will learn about network analysis and in particular clustering and functional enrichment analysis with a real world network example derived from the study of Autism Spectrum Disorders.
On Thursday we will be working through a notebook that performs functional enrichment analysis on the TCGA expression dataset that we were working with last week using the GSEAPy package.
If you have any problems please do ask by posting a question on the Piazza forum (you can privately message me on there if you do not want to ask a question in puclic). I will be monitoring the forum closely throughout the course and this is the best way to get in touch with me.
Remember that I’ve added some reading and reference material suggestions that some of you may like to refer to for some background biology information. This is entirely optional, you are not expected to read through all these, but may find them useful on occassion during the course. You can find these here.
In this course, you will learn how to use Python to retrieve and parse data from biological repositories through bulk download and application programming interfaces (APIs). You will learn about established data formats for different data modalities so that you can understand the structure and content of the data and how it was generated. Each week we will focus on analytical tasks in linked topics that span the main components of modern biomedical informatics research. Topics will change slightly each year, but will typically include tools, algorithms, and approaches for biological sequence, multi-omics (transcriptomics, proteomics, methylomics), biomedical network, and biomedical text analysis. Each topic will be explored using real-world examples.
On successful completion of this course, you should be able to:
- select sources of biomedical data appropriate for a given research question.
- determine the most suitable methods to use to analyse these data.
- implement and critically evaluate advanced Python code for biomedical data projects using reproducible research practices.