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Our work on (deep generative) disentanglement of information layers in single-cell data and virtually shifting cells across time/space/states! Nature Biotechnology go.nature.com/3U3pdMQ. Great work by Zoe Piran, w/ @NivCohenHuji, Yedid Hoshen. Beautiful art by Reo F

Cells simultaneously encode multiple signals, some overshadowed. Mor Nitzan Zoe Piran introduce SiFT (Signal FilTering), a kernel-based projection method, revealing underlying biological processes in single-cell data. Nature Communications #BiotechNatureComms nature.com/articles/s4146…

If you're passionate about developing multidisciplinary approaches that integrate concepts from mathematics, computer science, physics, network theory, ecology, and even economics to address challenges in tissue biology and medicine, we invite you to join our team!

We can often reconstruct a certain layer of information from single-cell data, like their spatial configuration or trajectory, but what can we learn when we peel that layer off? What else is encoded? SiFT is out Nature Communications, Led by Zoe Piran nature.com/articles/s4146… Hebrew University

Cells simultaneously encode multiple signals, some overshadowed. Here, Mor Nitzan, Zoe Piran introduce SiFT (Signal FilTering), a kernel-based projection method, revealing underlying biological processes in single-cell data. #BiotechNatureComms nature.com/articles/s4146…

Thank you Nature Communications for presenting our work Mor Nitzan, SiFT! Single-cell-verse--after reading 📑doi.org/10.1038/s41467… do give it a try 💻sift-sc.readthedocs.io, and peel off some known layers to reveal intricate biological signals!

Attention international postdocs: Applications for the Azrieli International Postdoctoral Fellowship are now being accepted and are due by May 9! The Azrieli International Postdoctoral Fellowship supports the best and brightest minds in their postdoctoral research in Israel,

Can we learn anything useful by merely observing how a deep learning network memorizes annotated genomic data? Absolutely, as we demonstrate in our new joint work with the Mor Nitzan lab, led by the gifted Jonathan Karin and Reshef Mintz.

In a new paper, we show how deep nets training dynamics can be used to refine our understanding (or annotations) of single-cell/spatial data and reveal facets of multicellular organization & function. Wonderful collaboration w Barak Raveh, led by Jonathan Karin and Reshef Mintz.

כתבה מאת ettay nevo שמספרת בין השאר גם על המחקר שלנו. תודה איתי!

It was such a wonderful conference! Thanks a lot Maria Brbic and Yvan Saeys for organizing

Attention prospective postdocs! The Azrieli International Postdoctoral Fellowship for Research at Israeli Universities in STEM, Humanities, and Social Sciences is now open for applications! You can apply today for the 2025–26 cohort. The Azrieli International Postdoctoral

I am excited to share that moslin, our work on single-cell lineage tracing, is out in Genome Biology! We use lineage tracing barcodes to relate cells across time points in an OT framework to study cellular state changes in development and regeneration doi.org/10.1186/s13059…

moslin combines lineage with state information to uncover cellular trajectories.

📢Jonathan Karin, Reshef Mintz, Barak Raveh and Mor Nitzan from Hebrew University introduce a tool for identifying erroneous annotations and ambiguous cell states, as well as for inferring trajectories from binary labels. nature.com/articles/s4358…

1/4 Annotatability is out in Nature Computational Science!  With Reshef Mintz, Barak Raveh and Mor Nitzan . nature.com/articles/s4358…

Introducing Annotatability: A new AI-powered framework by #HUJI researchers, led by Jonathan Karin, Reshef Mintz, Dr. Barak Raveh & Dr. Mor Nitzan, to tackle ambiguities in genomic data and improve single-cell analysis For the full article: phys.org/news/2025-01-f…

Featuring of our recent joint work with the Mor Nitzan lab, published in Nature Computational Science, on decoding complex biological signals by monitoring the dynamics of training artificial neural networks to memorize data annotations. Nice tweetorial by Jonathan Karin further below.

Our work on learning low-d models of spatiotemporal dynamics to better understand self-organizing patterns in biological systems, led by Matt Ricci Ricci Lab, with Guy Pelc - גיא פלץ, Zoe Piran , and Noa Moriel arxiv.org/abs/2412.03496 #ICLR2025

Matt Ricci Ricci Lab is opening his lab Institut Imagine in Paris! Highly recommended for those interested in merging dynamical systems and machine learning to better understand biological systems and interpret biomedical data