There are already libraries to do some of those things. There are for example several linear algebra libraries like gomatrix: http://code.google.com/p/gomatrix/
I don't know so much about bioinformatics, but I know some folks are using Go in that field without too much problem. Of course, the more specialized your needs, the greater the chances you might have to roll your own.
But given that Go 1 has hardly a few months old, this is not very surprising.
I'm working on a Restricted Boltzmann Machine learner for Go. (https://github.com/taliesinb/gorbm).
It is very early days (0.083 days to be exact) but I intend to support Hinton-style "deep learning" within the next few days.
I have a feeling that Go's concurrency features will make it a good fit for various ML workloads. And it's a rare combination of expressive and metal-close, which is good fit for prototyping crunchy algorithms.
These are things that, obviously, aren't going to be on the top of a core dev team's priority list. They will come with time. But I can't do without them.