We just published a paper in Chaos on ”Network topologies for maximal organismal health span and lifespan”, see aip.scitation.org/doi/10.106… The upshot is that scale-free #networks (as previously assumed) are optimal. #aging#complexaging
Just published "Efficient representations of binarized health deficit data: the frailty index and beyond" in Geroscience: rdcu.be/c4jzt Glen Pridham used PCA on binarized deficit data and showed how the FI (and more) naturally emerges! #aging#geroscience#frailty
It felt good to study similar measures (#robustness and #resilience) in different organisms (#mice and #humans) during #aging It feels like we can learn the most from model organisms when we can do parallel studies with human data (or vice versa) doi.org/10.7554/eLife.77632
We’ve just published a nice paper in eLife on #robustness and #resilience during #aging, using binarized data from mice and humans. Both robustness and resilience decline with age, but the timescales of action vary broadly over different health attributes. doi.org/10.7554/eLife.77632
ncl.ac.uk/futures/ageing-hea… Nice to see Newcastle recruiting at a senior level in #aginghealth. “This will require a multidisciplinary approach, drawing upon areas such as:
•data science
•artificial intelligence and machine learning
•software engineering”
So in #aging with studies of #frailty, you should use good imputation if you want calibrated (comparable) measures of FI between studies, or between populations, or between individuals — essentially if you want to (as precisely as you can) compare FI.
The idea is that while ignoring missing data still lets the FI (a summary measure of aging health) predict well, the ignored values are (with ignore) replaced with the individual average. This leads to a bias (or inaccuracy).
Congrats to Glen Pridham (PhD student) who just published a paper on the importance of imputation for precise FI (Frailty Index) studies link.springer.com/article/10…
Spencer found doi.org/10.1371/journal.pcbi… that the interactions between health attributes are well-modelled as linear and time-independent. It will be interesting to see how far that generalizes to other types of data and organisms.
Joint models of #aging predict both health trajectories and mortality — both are needed to compare to large population studies. It is too easy to just get mortality (#Gompertz) right…
Glad to say our #ML#aging paper has just been published by PLOS Computational Biology doi.org/10.1371/journal.pcbi… We call it the DJIN model of aging (dynamic joint interpretable network model) Congratulations Spencer Farrell, who also just received a PhD from Dalhousie!
Nice paper discussing how DAGs (directed acyclic graphs) push variance around, and how this can distort causal discovery benchmarks but may be exploitable IRL if variables have the same natural units. #APaperADayarxiv.org/abs/2102.13647