FitMultiCell

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FitMultiCell

FitMultiCell

@FitMultiCell

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

Sumali Eylül 2019
4 Sinusundan25 Mga Tagasunod
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Yannik Schälte
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. 🧵 --> arxiv.org/abs/2305.00506
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Yannik Schälte
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, ... emune-dev.github.io
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Yannik Schälte
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: biorxiv.org/content/10.110…
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Yannik Schälte
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. hsm.uni-bonn.de/events/hsm-sch…
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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: hcm.uni-bonn.de/events/eventpa…

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Yannik Schälte
Yannik Schälte@yannik_schaelte·
#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
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Yannik Schälte
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).
Simon Syga@sisyga91

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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Emad Alamoudi
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 🙂.
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morpheus
morpheus@morpheus_lab·
Check out the @EBItraining virtual course on 'Mathematics of life' with spatial modelling and reproducible modelling. Including a Morpheus tutorial!
EMBL-EBI Training@EBItraining

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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Yannik Schälte
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 biorxiv.org/content/10.110…. #YoungScientistsHMGU
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Tuklasin

@yannik_schaelte @morpheus_lab @JanHasenauer @JOSS_TheOJ @dennisprangle @StefanEngblom @A_Hellander @EmadAlamoudi