A huge thing in health care is currently the fact that many diagnostic tests turn up either (i) more false positives than true positives and (ii) diagnose things that turn out not to be true threats.

For (ii), see the debates about PSA tests. You may diagnose potential for cancer, but if the treatment causes more health problems than the disease; you've just created useless worry and costs with your test. The optimists see two things (1) better tests that could tell whether the prostate cancer is problematic and (2) as lifespans increase, the likelihood of you living long enough for the cancer to be a problem increases, so if we are trying to live forever, then the PSA test results will matter eventually.

Back to (i), the false positives. As a disease becomes more rare, the more your false positive rate matters. If a disease affects 1 in 10k people, your 0.1% FPR will diagnose 10 times as many people wrongly as it does correctly. This is pretty damn common with genetic tests.

I think you can get somewhere data mining whole genome sequences, particularly if you sequence from healthy and diseased tissues in individuals and do exome sequencing as well, but that doesn't appear to be what Color Genomics actually does.

This is a highly computer driven field, with tons of computer scientists working in genomics. This aspect is not something where they're any more likely to have interesting results than any of the academic labs around the country that are driving machine learning methods and working closely with those that are developing machine learning methods.

Far more interesting is the data the Color will collect if they grow a customer base, which is not something that many researchers have access to. However, if they're only collecting features and not the labels (cancer incidence) then it's pretty boring data.