Introduction to Bioinformatics & AI
Where biological data comes from, what AI actually is, and why the two have converged.
About this section
This opening section builds shared vocabulary before we touch any code. By the end you should be able to say what bioinformatics is, what people mean when they say “AI”, and — importantly — where each one fails.
Nobody here needs prior programming experience. What you do need is a biological question you care about.
What’s in this section
| Topic | You will be able to | |
|---|---|---|
| 1 | What is Bioinformatics? | Describe how sequence data is produced and what shape it arrives in |
| 2 | What is AI? | Distinguish AI, machine learning, and deep learning — and use the terms correctly |
| 3 | Where They Meet | Identify which biological problems AI genuinely helps with, and which it doesn’t |
| ✎ | Exercise | Classify real research questions and pick an appropriate method |
The through-line
A single claim connects everything in this workshop:
You cannot pick the right analysis until you know how the data was made. A sequencing artifact and a biological signal look identical in a spreadsheet. The difference lives in the wet lab, not the model.
This is why the section on AI comes after the section on where data comes from — and why the hands-on case study spends more time on study design than on the statistical test.
Before you start
Nothing to install for this section. Bring a laptop for the exercise, and a question from your own work — you’ll use it in the exercise.