As DNA reveals its secrets, scientists are assembling a new picture of humanity
Google is using graph learning for representations of stuff where obtaining a huge, representative, &quot;labeled&quot; corpus is hard.<p>As many in the field can tell you, &quot;labeling&quot; genomes is hard, too.  You can stick labels like &quot;died at 39 of AVCR&quot; or &quot;had 5 primary tumors by age 28&quot; or &quot;insane multi-substance abuser with extra toes&quot; on a person, but that doesn&#x27;t really encapsulate all their traits.  I claim that genome analysis is a great candidate for semi-supervised learning.  Looks like Ben Paten already had <i>that</i> thought...<p>Haussler, Paten, McVean, and the usual suspects are working on a tractable graph representation to replace (say) hg39, i.e. instead of a new &quot;reference genome&quot;, there should be thousands.  This makes more sense when you look at how common structural variants (say, inversions) are, and then when you do something like ask Haussler at ASHG &quot;how do we represent inversions in this thing?&quot; you realize how f<i>cking hard it really is to get it right.<p>McVean &amp; co. made it work for the major histocompatibility complex, which immunologists can explain better than I can, but that&#x27;s perhaps the most diverse bit of genome there is.  It&#x27;s riddled with ancient repeat element insertions and generally fascinating. It&#x27;s also a source of Little Problems like organ transplant rejections, graft vs. host disease in stem cell transplants, maybe bits of schizophrenia and neurodegenerative disease... in short, if it can be done for the MHC, it&#x27;s probably doable for the whole enchilada.  It&#x27;s not easy, though.  In fact it&#x27;s so difficult to get right that a sub-field of accurate HLA typing and immunotyping evolved in parallel to large-scale genome analysis, because it&#x27;s really, really important not to fuck this up.  So that&#x27;s a note of caution from historical precedent.<p>Nonetheless, it turns out that your pals at Google spearheaded the effort to have a Hadoop-like &quot;data lake&quot; for genomes (sign up for the GA4GH mailing lists if you like to watch professors bikeshed an API, and occasionally produce incredible insights by accident). Maybe this is going to converge in an interesting way.  It won&#x27;t happen overnight, but it will happen, and the mathematicians</i> will be vindicated.<p>* Seven Bridges is a little genomics company with a funny name.  Unless you&#x27;re familiar with Eulerian paths and the Seven Bridges of Konigsberg, in which case it&#x27;s sort of obvious why they chose their name.  Nice people, other than the patent, which infuriated most everyone else.