
Deep biomarkers of human aging: Application of deep neural networks - jonbaer
http://www.impactaging.com/papers/v8/n5/full/100968.html
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reasonattlm
A robust generally agreed upon and reliable biomarker of aging is a very
important thing that doesn't yet exist. It would ideally require something
simple like a blood sample, pin down biological age fairly accurately, and
work in mice, dogs, pigs, and people at the very least. The estimate of
biological age would be a measure of how damaged you are, and would have a
solid statistical relationship with remaining life expectancy.

Why is this important? Because at present the only way to assess a putative
rejuvenation therapy, such as the various approaches to senescent cell
clearance presently under development, is to try it in mammals and wait and
see. In mice that takes a lot of time and money, even when you work with one
of the suppliers who can sell you pre-aged-to-the-age-you-want mice for a few
hundred dollars each. Then never mind testing in longer-lived species. You'll
always be able to show that your therapy does what it says on the can (removes
X% of senescent cells in tissues A, B, and C) but how to prove that this does
in fact extend human life in the same way it does in mice, and by how much?

Testing putative rejuvenation therapies would run a lot faster if you could
just take a tissue sample a few weeks after the treatment, send it off to a
clinic, and quickly see that biological age was reduced. Then far fewer of the
wait and see studies would be needed, and the whole field would move a lot
faster.

At present DNA methylation patterns look pretty promising as a biomarker of
aging, but unfortunately there is still a way to go from the stage of pretty
promising to the stage of a generally agreed biomarker of aging that is good
enough to bypass the need for wait and see studies. So more signs of parallel
and different approaches in the research community are always welcome.

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joe_the_user
_The estimate of biological age would be a measure of how damaged you are, and
would have a solid statistical relationship with remaining life expectancy._

I think a claim that aging involves _only_ damage is reaching a bit. It seems
plausible that each standard phase of human development involve adaptation as
well as chromosome damage.

A standard claim is that evolve doesn't care about an organism once it is
beyond the age of reproduction. Human beings live considerably longer than
other primates and longer past the age of reproduction. One argument is that
the extra human lifetime is an adaption to allow an aged human to aid the
survival of their offspring. If this is mutation, then one might guess human
genes already contain adaptions again usual genetic decay. However this may
not be good news since _further extending_ these may be harder than just
eliminating the simple aging of simpler organisms.

I know that, just for example, the low metabolisms of the very elderly are
seen as a protection against cancer, cancer that would otherwise be more
prevalent given accumulative genetic. And this means a failure to lower
metabolism over aging time might actually be associated with higher mortality
(this may screw up the association of mortality and aging-process measures).

~~~
reasonattlm
The root cause is damage. That is the mainstream view in the research
community, for all that there is a lot of debate over which damage is actually
fundamental and which damage is more important, or how the damage progresses
in detail.

Then there is secondary and later damage caused by systematic dysregulation
resulting from the root cause damage. Then there is adaptation to damage,
primary and secondary and later, which is some cases is beneficial and some
cases not. E.g. epigenetic changes, stem cell quiescence, cardiovascular
remodeling, etc.

I'm vaguely optimistic about DNA methylation patterns as the basis for a
biomarker precisely because they are not damage, but rather a reaction to that
damage. In a way they are an evolved damage assessment, or at least they might
be used in that way. We'll see how it pans out in practice over the next five
years or so.

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thaw13579
The method seems interesting and potentially useful, but I'm skeptical of the
evaluation for two reasons. First, R2 is not a fair way to compare simple
(linear regression, kNN) and complex (deep neural net) models, as it will
always prefer a more complex model. Second, there was no indication how the
error was computed. Was there a hold out set or cross-validation for accuracy?
Without any more details, I have to assume they measured error on the the
training data, which will also prefer the more complex model.

Also, as a public service announcement:

"The authors are affiliated with Insilico Medicine, Inc, a commercial company
developing differential pathway activation scoring-based and deep learned
biomarkers of multiple diseases and aging and engaging in drug discovery and
drug repurposing."

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Houshalter
I'm a fan of deep learning, but is it really the best choice in an application
like this? I have always heard that shallow machine learning methods generally
did better on unstructured smallish data.

They should put this data on something like Kaggle.com and let researchers and
hobbyists around the world try to find the best model. At the very least
publish the data somewhere.

