Nick Phillips

Our new arXiv paper: We built a generative AI model of blood glucose levels from 10 million continuous glucose monitoring measurements of 11,000 people from the Human Phenotype Project We show that our model can predict clinical parameters such as liver-related parameters, blood lipids, and sleep-related indices. The model can also predict onset of future health outcomes even 4 years in advance, as well as outcomes of randomized clinical trials We also integrated dietary data into the models and show that the enhanced model can accurately generate blood glucose levels using only dietary intake data, simulate outcomes of dietary interventions, and predict individual responses to specific foods. The model also performs well on 15 different external datasets from 5 different geographical regions and 6 different continuous glucose monitoring devices Paper: arxiv.org/abs/2408.11876 Data: humanphenotypeproject.org Great work led by @GLutsker, Gal Sapir, @nastya_godneva, @smadarshilo, @H_Rossman in collaboration with @SamochaBonetD and @nvidia researchers @GalChechik @MannorShie @eli_meirom

Are there universal laws driving complex systems dynamics across scales? What kind of general theory connects fluctuations in microbiomes, rainforests or economic and urban systems? Check this @PNASNews paper by ashish george & @Jp_odwyer @sfiscience pnas.org/doi/abs/10.107…

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling dlvr.it/St16Cg

For some individuals, adding physical + heart activity can help explain glucose variance, but we only saw this for a subset of individuals. Thanks again to Tin-Hai and Felix for the help on this project, which has been a nice collaboration that started with our PHRT funding.(5/5)

We also found that some participants seem to display relatively large 24-hour oscillations in the underlying baseline glucose levels. (4/5)

Very happy that our article "Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling" is now online in Cell Reports Methods! (doi.org/10.1016/j.crme…) A brief overview... (1/5)

A few years ago we published a method on detecting oscillations in single-cell time series with @MagnusRattray @PapalopuluLab. We now have a basic implementation using Python and @GPflowProject ... Jupyter Notebook tutorial here -> nbviewer.org/github/Manches…

How precise are noisy #morphogen gradients in #tissue #patterning? It turns out that the positional information they convey to #cells in the #neuraltube is much more accurate than previously estimated! Our new #devbio paper is out in @NatureComms: rdcu.be/cH8Fx

🥳 Just awarded a Synapsis foundation grant ! ➡️Open PhD student position 🔬🧠Protein homeostasis in live single human neurons in healthy/ND disease. If you feel 🤩 tell me and apply at @epfl_edcb or EDMS PhD program. Thx for RT

We're gearing up for the final #PeakEnsemble of the year! Join us next month to hear from @NPSysBio of @Hopitaux_unige! 🇨🇭 #AI #datascience #datacommunity

@PabloRMier @mcmc_stan Glad you enjoyed it!

@NPSysBio @mcmc_stan Thanks for sharing that! very clear and illustrative

I've written a tutorial on Bayesian inference for single-cell gene expression data using STAN @mcmc_stan. We see how the Poisson, negative binomial and beta-Poisson mixture distributions emerge from simple stochastic models of gene expression dynamics. nbviewer.jupyter.org/github/naef-la…

Analyses of the #CircadianOscillator at the single-transcript level --> bit.ly/3qMJt2P from @Naefelix @EPFL_en #SingleCell #CircadianClock #QBio #transcription

smFISH of circadian clock genes + mixture models shows that circadian time constitutes only a small fraction of the total variability in mRNA number between cells. Our new pre-print @Naefelix @jakeyeung

First #PHRT PhD & Postdoc Day at @psich_en #protontherapy

OscoNet: Inferring oscillatory gene networks @MagnusRattray @Boukouva1Alexis biorxiv.org/content/10.110…

Our tightly collaborative work with @Naefelix and @NPSysBio is out - Transcriptional memory varies widely between genes and scales with expression variability - may play a role in tissue patterning nature.com/articles/s4146…

@AnzyMiller @PHRT_CH Thanks Anzy!

@NPSysBio @PHRT_CH Excellent news! Congrats!

Pleased to announce that I'll be starting a 2 year @PHRT_CH fellowship. We'll use Bayesian modelling, app data and transcriptomics to investigate the relationship between the time at which we eat, our circadian clock and metabolic disorders.