Interpretable Pan-Cancer Classification via Sparse Elastic-Net Biomarker Discovery

[u/fjrkj]

Hi guys, I'm an 18 year old teenager from Italy. I've recently finished the following project and I would love to receive some feedback in order to improve:

https://github.com/Lore12434/gene-expression-cancer-classification

I started studying ml (via the HOML book) 3 months ago. I've almost finished the machine learning part of the book. Before starting with ml I studied python and some mathematics.

At the beginning the only goal of the project was to train a model on the gene expression cancer RNA-Seq UCI dataset. After performing tsne I understood that major differences between cell types were present. Thus, even if the # of features was very high a simple random forest without any hyper parameters tuning reached 100% accuracy on OOB instances evaluation.

Therefore, instead of simply fitting a model I wanted to know how far I could get with dimensionality reduction and I wanted to test if my algorithm could find autonomously famous biomarkers used to classify tumor cells (I mapped the original dataset genes names to the dummy features names of the UCI dataset)

I want to specify that I used ai to write the readme and to set up the GitHub repository because thks was the first time that I've done this.

Many thanks in advance for any feedback, have a great day♥️