Nick Phillips
@NPSysBio
Researcher at Hôpitaux Universitaires de Genève (HUG). Using Bayesian data analysis to extract personal parameters from wearable device data.
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…
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
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
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
The circadian oscillator analysed at the single-transcript level biorxiv.org/cgi/content/sh… #biorxiv_sysbio
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…
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.