I think we need to stop this useless debate as clearly one side has often no clue about the other. This behavior has led to massive failure in my career to successfully launch IVD devices or have a successfully bioinfo software for physicians - talking from experience here.
Success in biological research is driven by the scientist's ability to sort wheat from all this chaff, and it's acquired by gathering the data themselves hands-on in wet labs. A master biologist has learned how to navigate that space experimentally and analytically using techniques they've mastered just well enough to see over the noise.
As someone with degrees in both bio and CS and 15 years of work that crosses the boundary between them, I'm decidedly more in awe of those who have mastered biology.
I think that you and a lot of people in this thread don't understand the impossibility of understanding millions of lines of code. Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?
No, but I think a group of geeks could. However a group of biologists cannot explain a biological system in full. When I say a computer system can be understood in full, I mean that human knowledge encompasses the working and programming of the machine, not that one person could know every detail. This is not the case for biology, as there's plenty we don't know.
> Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.
It really doesn't provide a complete understanding for biology anymore than it does for sociology. It's not possible for humans to reductively explain such fields in terms of physics. No one can even prove this is possible. But it's not a complete understanding for physics either, since there's Relativity and questions about quantum gravity and dark energy.
Not only is documentation pretty scarce these days, but good information on the interactions between pieces of software is much scarcer, due to the exponential number of ways that it can interact.
How do you find info on bio topics right now?
The trick is interpreting it and putting it into context. A paper will report the results of one specific experiment, and it’s very rarely exactly what you want. Understanding how a result will generalize to other conditions is tricky, even for experts: there are tons of weird feedback loops, unusual dynamics, and other traps for the unwary (plus badly designed experiments and the occasional legit Type I error). For example, doubling the amount of a substance almost never doubles its effect, and in some cases, the effects aren’t even monotonic: ~75% alcohol, for example, is a much better disinfectant than 50 or 100%.
With time—-and lots of paper-reading, you do eventually develop a sense for what factors might matter and how you could check.