aiGrunn: fighting cancer with AI - Hylke Donker¶

Tags: aigrunn, ai, python

(One of my summaries of the 2023 Dutch aiGrunn AI conference in Groningen, NL).

What is cancer? According to wikipedia: abnormal cell growth with the potential to invade or spread to other parts of the body. That is what you can observe. Medically, there are several aspects of cancer:

• It prevents the cell from dying.

• It can grab more than usual resources.

• No sensitivity to the regular anti-growth signals.

• Etc.

AI starts getting used in clinics. For instance for proton therapy: where to best apply the proton radiation. And in radiology: letting AI look at images to detect cancer. A good AI can out-perform doctors. Analysis of blood samples, trying to detect cancer based on the DNA samples in there.

DNA mutations can also be detected, which is what he focuses on. Cancer is basically a “desease of the genome”. DNA is made up of T, C, G and A sequences. Technically, it is perfectly feasable to “read” DNA.

How do mutations occur? Exposure can leave “scars” in DNA. Damage can occur due to sunlight or smoking for instance. Specific sources result in specific kinds of damage: smoking has a “preference” for changing specific letters. With analysis, you can thus detect/estimate the cause of cancer.

A method to detect it is non-negative matrix factorisation. Normally you can only summarize the data in “hard” clusters: something is either A or B. With this technique, you can do “soft” clusters: something can be a little bit A and a bit more B.

Matrix factorisation is a way to relate separate data sources. For movies, you can have persons with preferences for comedy or aciton movies. And movies with a percentage action/comedy. Combined you get a matrix with estimates for the preference for every movie per user.

In a similar way, he creates a matrix relating cancer causes (like smoking) to specific observed types of DNA damage.

But… how reliable are the results? You can treat the matrix as a neural network. You can then use bayesian analysis to assess the probabilities.

He made a python packge for his reasearch: “mubelnet”. Sourcecode here: https://gitlab.com/hylkedonker/mubelnet and here is an article. (Thanks Hylke for sending some links as I couldn’t find the project initially!)

AI is transforming cancer care. The only part it doesn’t affect is the actual nursing process.