FitMultiCell

FitMultiCell, an integrated platform for data-driven modeling of multi-cellular processes.

[Yannik Schälte @yannik_schaelte]

❓How can we speed up ABC on parallel infrastructure?
📜Happy to present our preprint on look-ahead sampling - a wall-time minimizing parallelization strategy for approximate Bayesian computation.
🧵 --> https://t.co/fEAGUJzZlN

[Emad Alamoudi @EmadAlamoudi]

Multiscale models are helpful to understand the processes determining tissue dynamics. However, inferring such models is difficult. Here is our new #preprint with @yannik_schaelte, @morpheus_lab @JanHasenauer and many other great collaborators.
Manuscript: https://t.co/XUMbl7Ux27

[Emad Alamoudi @EmadAlamoudi]

How we can massively parallelize likelihood-free parameter inference for biological multi-scale systems? Happy to share our NIC proceedings at: https://t.co/EY5brweKGu with the help of @yannik_schaelte ,@JanHasenauer and many others from @FitMultiCell consortium.

[Yannik Schälte @yannik_schaelte]

Enabling mechanistic understanding of infectious diseases using invertible neural networks - Lots of great progress + discussions at our #EMUNE meeting in Heidelberg. 🪅

w/ Ullrich Köthe, Lutz Brusch, @JanHasenauer, Frederik Graw, Noemi Castelletti, ...
https://t.co/3qepehBdEK

[Yannik Schälte @yannik_schaelte]

📜How can we handle missing data, which are ubiquitous in experimental studies, when performing amortized parameter inference via neural posterior estimation? Happy to present our #preprint with Zijian Wang and @JanHasenauer.

➡️Manuscript: https://t.co/nlBim8yBZ3

[Yannik Schälte @yannik_schaelte]

Very much enjoyed this 🌞summer school at @HCM_Bonn!
👩‍🏫Amazing lectures on stochastic models and inference; 👩‍🎓awesome contributed presentations; 🔧practical tool sessions; 🎇great discussions sparking up insights and coops; 👥inspiring panels; 🏃‍♀️fun hikes.
https://t.co/SmkE3TTBny

[Yannik Schälte @yannik_schaelte]

📜 Here is our paper on #pyABC, published in @JOSS_TheOJ.

➡️Paper: https://t.co/XowjY6U0pi
➡️Tool: https://t.co/Ewj2bmWmPg

🧵 We describe various updates to our likelihood-free parameter inference tool.

[Yannik Schälte @yannik_schaelte]

🎒Summer school: 𝐈𝐧𝐯𝐞𝐫𝐬𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐟𝐨𝐫 𝐦𝐮𝐥𝐭𝐢-𝐬𝐜𝐚𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐬

💬With: Linda Petzold, Christiane Fuchs, @dennisprangle, @StefanEngblom, @A_Hellander

⏰August 22-26
📌@HCM_Bonn

✏️Details+register: https://t.co/PcpNqSw0F3

[Yannik Schälte @yannik_schaelte]

🧑‍💻 psst here is our new #pyABC #preprint quickly wrapping up various updates & new features! 🥧🔤
https://t.co/15igJm5iiS

Thanks esp. to Emmanuel Klinger, @EmadAlamoudi , @JanHasenauer, and many users and collab partners!

[Yannik Schälte @yannik_schaelte]

📜 Excited to share our new preprint on accounting for informativeness in heterogeneous data via inverse #ML models in likelihood-free #ABC parameter inference!
⬇️🧵 1/n
https://t.co/LnFTD9hdxa
@JanHasenauer @CompHealthMuc @LIMES_Bonn @FitMultiCell

[Yannik Schälte @yannik_schaelte]

Happy to release #pyABC 0.12! 👶 https://t.co/VYXugH6Blb
🧵Featuring a bunch of new features:

[Yannik Schälte @yannik_schaelte]

Here is our latest preprint on robust adaptive distances for ABC on outlier-corrupted data, based on a method by @dennisprangle. All methods are available in #pyABC. @EmadAlamoudi @JanHasenauer.

➡️ Preprint: https://t.co/fRzwsRa8ew
➡️ Example notebook: https://t.co/rjIqKcy1Bg

[Yannik Schälte @yannik_schaelte]

#pyABC 0.11.0 is out! https://t.co/mcCEJSb1P1 🎉

Featuring (esp., see changelog):
- robust adaptive distances
- speed-ups (><50%) by improved parallelization and more efficient transitions
- overhauled logging
- better #PEtab support
- many fixes and improvements

[Yannik Schälte @yannik_schaelte]

Happy to share our preprint led by @sisyga91. Bayesian inference and a network-based epidemiological model combining local and random transmissions suggest reducing out-of-cluster contacts to reduce #COVID19 spread, inducing a linear instead of exponential regime (R≈1).

[Emad Alamoudi @EmadAlamoudi]

Looking forward to present our work on simulating and parameterizating computational models of multi-cellular processes (@FitMultiCell ) today at #IbSB2020 at 14:40 CET 🙂.

[morpheus @morpheus_lab]

Check out the @EBItraining virtual course on 'Mathematics of life' with spatial modelling and reproducible modelling. Including a Morpheus tutorial!

[FitMultiCell @FitMultiCell]

Some compiled information on the recent project on exact ABC inference with noise!

[Yannik Schälte @yannik_schaelte]

Looking forward to present today at #ismb2020 at 03:20 PM EDT our work on efficient exact ABC!

➡️ The slides: https://t.co/T2FAHPJTIq
➡️ Example notebook: https://t.co/Z8OwTFazx7
➡️ Paper: https://t.co/EwW3QOU4dI

[Yannik Schälte @yannik_schaelte]

Excited to see my first paper on efficient ABC parameter inference with noisy data out in @OUPBioinfo #ismb2020! #bioinformatics @JanHasenauer @FitMultiCell https://t.co/nSjv0swVs0

[Yannik Schälte @yannik_schaelte]

4/4 In our latest (recently accepted) work, we show how ABC can actually give efficient exact inference for models with noisy measurements. Here is a little "graphical abstract".

➡️ For more info, see the preprint https://t.co/pDoFa3Rosd. #YoungScientistsHMGU

[Yannik Schälte @yannik_schaelte]

3/4 We @JanHasenauer lab @ICBMunich developed the #HPC ready tool #pyABC (https://t.co/VYXugH6Blb), with e.g. self-tuned distances and proposal kernels. Together with @morpheus_lab we develop the @FitMultiCell platform tailored to multi-cellular systems. #YoungScientistsHMGU