Machine Learning Reveals Genetic Controls
An anonymous reader writes with this quote from Quanta Magazine:
Most genetic research to date has focused on just 1 percent of the genome — the areas that code for proteins. But new research, published today in Science, provides an initial map for the sections of the genome that orchestrate this protein-building process. "It's one thing to have the book — the big question is how you read the book," said Brendan Frey, a computational biologist at the University of Toronto who led the new research (abstract).
For example, researchers can use the model to predict what will happen to a protein when there’s a mistake in part of the regulatory code. Mutations in splicing instructions have already been linked to diseases such as spinal muscular atrophy, a leading cause of infant death, and some forms of colorectal cancer. In the new study, researchers used the trained model to analyze genetic data from people afflicted with some of those diseases. The scientists identified some known mutations linked to these maladies, verifying that the model works. They picked out some new candidate mutations as well, most notably for autism.
One of the benefits of the model, Frey said, is that it wasn’t trained using disease data, so it should work on any disease or trait of interest. The researchers plan to make the system publicly available, which means that scientists will be able to apply it to many more diseases.
For example, researchers can use the model to predict what will happen to a protein when there’s a mistake in part of the regulatory code. Mutations in splicing instructions have already been linked to diseases such as spinal muscular atrophy, a leading cause of infant death, and some forms of colorectal cancer. In the new study, researchers used the trained model to analyze genetic data from people afflicted with some of those diseases. The scientists identified some known mutations linked to these maladies, verifying that the model works. They picked out some new candidate mutations as well, most notably for autism.
One of the benefits of the model, Frey said, is that it wasn’t trained using disease data, so it should work on any disease or trait of interest. The researchers plan to make the system publicly available, which means that scientists will be able to apply it to many more diseases.
See, the problem is many of you don't get that what you think of as "noise" in the DNA is actually code. Shifted code. The internal mechanisms use cis regulation and miRNA, mRNA, cRNA to adapt to things going on in the environment.
It's not noise code, or broken code.
It's designed to do that.
If anyone had taken assembler and machine coding back in the old days of computing, they'd get it. You only have so much to code with, so you make it do multiple things.
-- Tigger warning: This post may contain tiggers! --
We let the machines reverse engineer the homo sapien genome. Next step, a vaccine to eradicate the infection from the planet.
Have gnu, will travel.
Junk seems to be amazingly capable. I seem to be learning of its doing more and more with each passing day. Impressive stuff.
I don't really feel like I have any sort of disease.
I love how everybody but autistic people want to cure autism.
I mean we very seldom lie or use subterfuge, and yet WE'RE the ones that need to be cured? And what about the awesome abilities some of us have with math and various things? For all anyone knows, we're the next step on the evolutionary ladder and the only cost is a little special attention when we're children.