> Computer scientists should be involved in the R&D process from its very beginning, not just as sophisticated data analysts. Starting from the design of the wet-lab experiments — where the data is being generated. This is because we, as computer scientists understand that data is the most important resource. Your model, whether it is an incredibly sophisticated CGAN or a logistic regression is as good as your data.

This is the key point in the article. There's lots of bioinformatics and computational biology work being done, but the vast majority of it winds up being of questionable use- either because the researcher didn't understand the biological system well enough to make an appropriate model, or because the biologists at the bench didn't understand the analytical problems well enough to collect the right kind of data.

The typical model of research is "design experiment -> collect data -> analyze data -> results", and when each step is being done by different people, with different training and no input into each other's work, it's tremendously wasteful. It's the science equivalent of the waterfall method, except that instead of technical debt you wind up with dubious publications.