
Jovan Tanevski
@tanevski
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Group leader - computational biomedical discovery - @UniHeidelberg, @uniklinik_hd. Associate - knowledge technologies - @JSI_SLO. Visitor - @emblebi.
Heidelberg, Germany
Joined December 2008
This work was led by @FCeccarelli96 in collaboration with @pl219_Cambridge, @SeanHoldenAI, @saezlab and Tanevski Lab.
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RT @felixjhartmann: Together with @tanevski and @drbetge, we are looking for a computational postdoc interested in the combination of model….
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RT @natgabrielli: Tomorrow at BioQuant : Dissecting and overcoming different shades of cancer immune evasion by Benjamin Izar. Join us! Sup….
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I am particularly happy for this publication. It is a milestone in my recently started transition to a group leader. I am grateful to everyone involved - @Arezou_Rahimi for her amazing work and trust, @JulioSaezRod for his continuous support and the great collaboration with @GSK.
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In a very busy area with many great methods, we took a step forward. We generalize on localization and deconvolution and introduce new tasks, bridging information between samples and across modalities. We offer a flexible and scalable solution while achieving the best performance.
1/n Our multi-objective optimization framework for transferring features across single-cell and different resolutions of spatial omics DOT is now published @NatureComms 🎉, freely available as an open source R implementation at
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🚨 We are looking for a PhD student 🚨 to join the Tanevski Lab and @DenisSchapiro Lab at the Heidelberg University Hospital, working at the intersection of machine learning and spatial omics as part of the Translational Spatial Profiling Center. Please RT
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RT @emblebi: We welcome Julio Saez-Rodriguez @JulioSaezRod as our new Head of Research!. Associate Director for EMBL-EBI Services Jo McEnty….
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If this sounds interesting to you and you happen to look for a new position, with @felixjhartmann we are hiring an interinstitutional postdoc to work with us in Heidelberg on capturing and exploring higher order tissue organization from highly multiplexed omics data.
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If you like MISTy you'll really like Kasumi Kasumi is a multi-view explainable representation learning approach, capturing localized relationship-based patterns in tissues across conditions aiming towards facilitating translation.
Local heterogeneity of tissues can define their function and predict clinical outcomes. We introduce Kasumi 💻to identify spatially localized neighborhoods of intra and intercellular relationships from spatial omics persistent across samples & conditions
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Amazing work by @DanielBDimitrov extending LIANA to now include also CCC approaches aimed at spatial data. I’m happy that i was able to help by showcasing MISTy. Huge thanks to @psl_schaefer for the adaptation, bringing MISTy to the Python leaning part of our community.
📣 Introducing LIANA+: an all-in-one cell-cell communication (CCC) framework .🧬 Many tools exist, yet they use various syntaxes and are tailored for a specific purpose.LIANA+ harmonises and extends existing methods, enabling their synergistic applications
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