@FitMultiCell

FitMultiCell, an integrated platform for data-driven modeling of multi-cellular processes. https://xitter.cf/t.co/a882oHPKTE

Joined September 2019
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  • Followers25
❓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. 🧵 --> arxiv.org/abs/2305.00506
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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: biorxiv.org/content/10.1101/…
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How we can massively parallelize likelihood-free parameter inference for biological multi-scale systems? Happy to share our NIC proceedings at: juser.fz-juelich.de/record/9… with the help of @yannik_schaelte ,@JanHasenauer and many others from @FitMultiCell consortium.
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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, ... emune-dev.github.io/
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📜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: biorxiv.org/content/10.1101/…
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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. hsm.uni-bonn.de/events/hsm-s…
Yannik Schälte@yannik_schaelte9 Jun 2022
🎒Summer school: 𝐈𝐧𝐯𝐞𝐫𝐬𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐟𝐨𝐫 𝐦𝐮𝐥𝐭𝐢-𝐬𝐜𝐚𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 💬With: Linda Petzold, Christiane Fuchs, @dennisprangle, @StefanEngblom, @A_Hellander ⏰August 22-26 📌@HCM_Bonn ✏️Details+register: hcm.uni-bonn.de/events/event…
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📜 Here is our paper on #pyABC, published in @JOSS_TheOJ. ➡️Paper: doi.org/10.21105/joss.04304 ➡️Tool: pyabc.readthedocs.io/en/deve… 🧵 We describe various updates to our likelihood-free parameter inference tool.

pyABC: Efficient and robust easy-to-use approximate Bayesian computation

Schälte et al., (2022). pyABC: Efficient and robust easy-to-use approximate Bayesian computation. Journal of Open Source Software, 7(74), 4304, https://doi.org/10.21105/joss.04304

joss.theoj.org
JOSS@JOSS_TheOJ25 Jun 2022
Just published in JOSS: 'pyABC: Efficient and robust easy-to-use approximate Bayesian computation' doi.org/10.21105/joss.04304
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🎒Summer school: 𝐈𝐧𝐯𝐞𝐫𝐬𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐟𝐨𝐫 𝐦𝐮𝐥𝐭𝐢-𝐬𝐜𝐚𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 💬With: Linda Petzold, Christiane Fuchs, @dennisprangle, @StefanEngblom, @A_Hellander ⏰August 22-26 📌@HCM_Bonn ✏️Details+register: hcm.uni-bonn.de/events/event…
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🧑‍💻 psst here is our new #pyABC #preprint quickly wrapping up various updates & new features! 🥧🔤 arxiv.org/abs/2203.13043 Thanks esp. to Emmanuel Klinger, @EmadAlamoudi , @JanHasenauer, and many users and collab partners!

pyABC: Efficient and robust easy-to-use approximate Bayesian computation

The Python package pyABC provides a framework for approximate Bayesian computation (ABC), a likelihood-free parameter inference method popular in many research areas. At its core, it implements a...

arxiv.org
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📜 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 biorxiv.org/content/10.1101/… @JanHasenauer @CompHealthMuc @LIMES_Bonn @FitMultiCell
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Happy to release #pyABC 0.12! 👶 github.com/icb-dcm/pyabc 🧵Featuring a bunch of new features:

GitHub - ICB-DCM/pyABC: distributed, likelihood-free inference

distributed, likelihood-free inference. Contribute to ICB-DCM/pyABC development by creating an account on GitHub.

github.com
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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: biorxiv.org/content/10.1101/… ➡️ Example notebook: pyabc.rtfd.io/en/develop/exa…
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#pyABC 0.11.0 is out! github.com/ICB-DCM/pyABC 🎉 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

GitHub - ICB-DCM/pyABC: distributed, likelihood-free inference

distributed, likelihood-free inference. Contribute to ICB-DCM/pyABC development by creating an account on GitHub.

github.com
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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).
Simon Syga@sisyga918 Dec 2020
Our new preprint is out! We inferred the effects of interventions on #COVID19 transmission networks. Preventing non-local contacts is key to stop the exponential spread! Thanks @DianaDavidRus1 @yannik_schaelte @M3sBiomath for the great work! arxiv.org/abs/2012.03846
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Looking forward to present our work on simulating and parameterizating computational models of multi-cellular processes (@FitMultiCell ) today at #IbSB2020 at 14:40 CET 🙂.
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FitMultiCell retweeted
morpheus@morpheus_lab2 Sep 2020
Check out the @EBItraining virtual course on 'Mathematics of life' with spatial modelling and reproducible modelling. Including a Morpheus tutorial!
EMBL-EBI Training@EBItraining16 Jul 2020
Join us #virtually this September. The full programme for 'Mathematics of life' can now be found on our website. It features talks, tutorials and plenty of opportunity for group projects. #EBImaths 💻 bit.ly/EBImaths ⏰ Applications close 14 Aug 2020

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Some compiled information on the recent project on exact ABC inference with noise!
Yannik Schälte@yannik_schaelte16 Jul 2020
Looking forward to present today at #ismb2020 at 03:20 PM EDT our work on efficient exact ABC! ➡️ The slides: yannikschaelte.github.io/pre… ➡️ Example notebook: pyabc.readthedocs.io/en/late… ➡️ Paper: academic.oup.com/bioinformat…
Presentation front slide

ALT Presentation front slide

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Excited to see my first paper on efficient ABC parameter inference with noisy data out in @OUPBioinfo #ismb2020! #bioinformatics @JanHasenauer @FitMultiCell academic.oup.com/bioinformat…

Efficient exact inference for dynamical systems with noisy measurements using sequential approxim...

AbstractMotivation. Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and

academic.oup.com
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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 biorxiv.org/content/10.1101/…. #YoungScientistsHMGU
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3/4 We @JanHasenauer lab @ICBMunich developed the #HPC ready tool #pyABC (github.com/icb-dcm/pyabc), with e.g. self-tuned distances and proposal kernels. Together with @morpheus_lab we develop the @FitMultiCell platform tailored to multi-cellular systems. #YoungScientistsHMGU
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