Nick Phillips

Researcher at Hôpitaux Universitaires de Genève (HUG). Using Bayesian data analysis to extract personal parameters from wearable device data.

[Eran Segal @segal_eran]

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: https://t.co/KbBo3DLnsa
Data: https://t.co/l9V3k0IuzY

Great work led by @GLutsker, Gal Sapir, @nastya_godneva, @smadarshilo, @H_Rossman in collaboration with @SamochaBonetD and @nvidia researchers @GalChechik @MannorShie @eli_meirom

[Ricard Solé @ricard_sole]

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 https://t.co/1ZBSmzXZli

[Cell Reports Methods @CellRepMethods]

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling https://t.co/PHg5oB75Vz

[Nick Phillips @NPSysBio]

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)

[Nick Phillips @NPSysBio]

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! (https://t.co/XUVZZ5SwFB) A brief overview... (1/5)

[Nick Phillips @NPSysBio]

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

[Nick Phillips @NPSysBio]

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 -> https://t.co/jUCTVmiivz

[Dagmar Iber @DagmarIber]

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: https://t.co/oIkk5R7LF9

[David Suter @davidsuter_epfl]

🥳 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

[Peak @Peak_HQ]

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

[Nick Phillips @NPSysBio]

@PabloRMier @mcmc_stan Glad you enjoyed it!

[Nick Phillips @NPSysBio]

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. https://t.co/Qq4lSImAwe

[Mol Syst Biol @MolSystBiol]

Analyses of the #CircadianOscillator at the single-transcript level --> https://t.co/ifyn0mSQ1h from @Naefelix @EPFL_en
#SingleCell #CircadianClock #QBio #transcription

[Nick Phillips @NPSysBio]

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

[bioRxiv SysBio @biorxiv_sysbio]

The circadian oscillator analysed at the single-transcript level https://t.co/QkwYqFgGM9 #biorxiv_sysbio

[Personalized Health and Related Technologies @PHRT_CH]

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

[luisa @luisa_cutillo78]

OscoNet: Inferring oscillatory gene networks @MagnusRattray @Boukouva1Alexis https://t.co/E12gGBC7Jp

[David Suter @davidsuter_epfl]

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
https://t.co/HuASPACHmo

[Nick Phillips @NPSysBio]

@AnzyMiller @PHRT_CH Thanks Anzy!

[Nick Phillips @NPSysBio]

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.