
Aleks Goeva
@aleksgoeva
Show me your neighbors and I'll tell you who you are - the single cell transcriptomics edition. Statistician, postdoc @broadinstitute, @MIA_at_Broad, @TheHDSR
ID: 1311397826866315264
https://tudaga.github.io/ 30-09-2020 20:09:48
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New from Harvard Data Science Review Early Career Board: Aleks Goeva, Ana Trisovic, K Blu, & I made a zine about Jessica Hullman and Andrew Gelman et al.'s paper "Designing for interactive exploratory data analysis requires theory of graphical inference" Thanks @sarahmirk for the zine tutorial!



Miri Adler, Noa Moriel, Aleks Goeva, Evan Macosko, Aviv Regev, Ruslan Medzhitov, & Mor Nitzan - Emergence of Division of Labor in Tissues through Cell Interactions and Spatial Cues - how do cells decide what tasks to perform? - youtu.be/MmI7ci_nGzw





Just one among many reasons Learning Meaningful Representations of Life is proudly virtual at NeurIPS Conference this year. Submit your abstract by Sep 30, 2022: lmrl.org/submit

So excited for this Wednesday’s #BroadMIA when David Kelley and Carl de Boer will tells us what’s new in using #DeepLearning to understand #GeneRegulation!

Looking forward to my Models, Inference & Algorithms at Broad talk tomorrow, and honoured to be featured alongside David Kelley . Thanks for hosting, Aleks Goeva !


So excited for Oana Ursu (@[email protected]) and Charlotte Bunne’s talks this Wednesday at #BroadMIA! Tune in at 9AM EST to learn about computational approaches to study single-cell state transitions and responses to perturbation!


MIA Spring ‘23 Speaker Series ⏰ April 5, 9AM EST 💬 Lucy Colwell, Benjamin Sanchez-Lengeling Ben Sanchez-Lengeling 💡Primer: Intro to machine learning for molecules 💡Seminar: ML to predict protein function from sequence ... 📬 Join our mailing list to get the talk link! #BroadMIA



Interested in uncovering biological processes in single-cell data? Check out SiFT, featured in Nature Communications. SiFT peels off layers of known variability and allows studying previously unexplored bio signals. Try it yourself: sift-sc.readthedocs.io. w/ Mor Nitzan Artwork: Reo F.


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