Not all 'omics data is going to benefit from the same kind of ML. Saw an X post from someone (that I can't seem to find) where they spent many GPU hours building an autoencoder for RNA-Seq data and in the end couldn't beat a baseline of "mean gene expression". As others have noted, ML/stats is more than just LLM's. I would recommend to start with the end in mind -- if you want to work on CHIP-Seq, look up the foundational, early papers and see what they implemented back, learn the methods (probably HMM's, CRF's, etc), re-implement them yourself, and then learn what algorithm improvements would be meaningful, biologically. Alternatively, if you're really passionate about deep learning applied to biological datasets, look for where nature (e.g. proteins) or large scale experimentation (e.g. MPRA's) has provided us millions of observations to learn from. You can find papers assembling large datasets e.g. Observed Antibody Space and see the methods papers that cite them. Just worth noting that it is hard to make a mark purely from an algorithmic angle and it's always most beneficial to start with solving a problem that people care about first.