Yes, the title is true even if I do data science in bioinformatics, I don't do machine learning. As seen recently if used correctly, regressions tend to work as well as machine learning. Classic tools (?) still work, I can't say I have tried all of them, but they are quite useful. Also in bioinformatics it is hard to get a big number of samples to make both a good and reliable generalization and to train reliable a model with enough confidence. Last, most machine learning methods are to me black boxes, I don't understand them (yet). I like to understand what I use. (Although I can't say I have deeply understood the differences between some regression methods I use). Then, why I am writing this? Because it seems like an hype to say things like "powerful network medicine tools", "machine learning model", without explaining them in detail. So it becomes a black box, and science is not about black boxes. In science we want to increase the know...
About bioinformatics, science and some personal achievements around bioinformatics