Tired Topics

Bioinformatics suffers quite a lot from fads and bandwagons. Does this happen as much in other scientific disciplines, I wonder?

It's not difficult to do a literature search to find out how much work there has been done in a particular field already; either people do these but ignore the results, they're convinced that they can add some valuable insight (experience says: probably not) or they've started a project and can't back out of publishing because they've already got too much invested in it. Why do journals keep publishing this stuff?

Anyway, if I never read a paper on any of the following topics again, I'll be a happy man (not least because I'm guilty of dabbling in one or two of them myself):

• Anything to do with analysing microarray data using GO terms : this was a good idea three years ago. Finding overrepresented GO terms - how many web based systems to do this do we need?
  
• Grouping proteins somehow to help automatically annotate genes : unless the specificity of your system is a lot better than what already exists, please keep it to yourself. Poor quality annotation is far worse than no annotation at all. Sometimes I swear the only difference between some systems is the tortured acronym used for a name.
  
• Text mining for protein interactions : does anybody actually trust this kind of data?
  
• Analysing the structure of protein interaction networks : they're scale free! They're not scale free! Nobody knows! If current protein interaction networks are actually just incomplete graphs of all possible interactions (under a variety of different conditions, given the combined networks used nowadays) then how relevant to actual biological processes are any such analyses be anyway?
  

Bioinformatics is a fast moving subject, but sometimes it's just not fast moving enough.