Alex Wolf

@falexwolf

Machine Learning | Single Cell | Scanpy | Head of Applied Machine Learning at Cellarity

Vrijeme pridruživanja: studeni 2016.

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  1. proslijedio/la je Tweet
    9. sij

    If you're interested in who will be a future scientific leader, keep an eye on the PhD students who highlight, promote, and applaud the work of their competitors.

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  2. 7. sij

    Many thanks to the authors of BBKNN from Teichmann Lab (in particular, Krzysztof Polański) for integrating the tool so well into Scanpy and letting us use it as a reference in the tutorial. Here are the original sources:

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  3. 7. sij

    Scanpy 1.4.5 is out with an overhauled website and a long-overdue first tutorial on dataset integration. Most contributions from Scanpy core (, , , ) with the "ingest" tool due to .

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  4. 10. pro 2019.
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  5. proslijedio/la je Tweet
    14. stu 2019.

    Super happy that our open-source modular toolkit for single cell genomics analyses was selected by for funding to expand core infrastructure and build a true community platform. This alongside giants such as -learn and . 😅

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  6. proslijedio/la je Tweet
    29. lis 2019.

    Excited to share our scVelo manuscript - led by and , we generalize the beautiful RNA velocity concept from and to transient cell states through dynamical modeling. and

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  7. proslijedio/la je Tweet
    7. lis 2019.

    Excited to share our recent work on generative modeling for unpaired data using a ‚transformer VAE’. Led by and , we extend scGen using a conditional VAE together with an MMD regularization. Applications for images and scRNA-seq.

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  8. 7. lis 2019.

    Our (with , , ) new end-to-end-trained backend for scGen is out: transformer VAE. Predict how gut cells respond to infection, immune cells to stimulation, or how people would look with a smile (😐 -> 🙂).

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  9. proslijedio/la je Tweet
    23. ruj 2019.

    Proudly carrying Scanpy decal on my mac, an incredibly useful python package developed by my colleague . I wish I had such a tool 4 years ago when I was building data integration solutions with Deep Learning.

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  10. proslijedio/la je Tweet
    14. kol 2019.
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  11. proslijedio/la je Tweet
    29. srp 2019.

    Happy that our tool scGen just got published in . We show that generative models predict single-cell perturbation effects out-of-sample - great work led by and ! Try it at , read at

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  12. 12. tra 2019.
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  13. 2. tra 2019.

    "Overall, we found a large diversity across the four evaluation criteria, with only a few methods, such as PAGA, Slingshot, and SCORPIUS, performing well across the board (Fig. 2b)." 😀 From the TI review about

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  14. proslijedio/la je Tweet
    1. tra 2019.

    Unsure which of the more than 70 trajectory inference method to use for your single-cell dataset? The benchmarking effort led by and is now published at .

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  15. proslijedio/la je Tweet
    27. ožu 2019.

    Awesome talk by David on pseudodynamics and diffxpy. Finally a malleable differential expression toolbox for python!

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  16. proslijedio/la je Tweet
    26. ožu 2019.

    Using PAGA, elucidates the origin of pancreatic beta cells, which are crucial for producing insulin, in an STZ mouse model of diabetes

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  17. 26. ožu 2019.

    Since today, Scanpy has a logo with a cellular decomposition of a Scampi in it. 😀

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  18. proslijedio/la je Tweet
    21. ožu 2019.
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  19. proslijedio/la je Tweet
    10. ožu 2019.

    A view in log scale of the metrics of the software for analysis, with SCANPY clearly in the lead

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  20. 20. ožu 2019.

    Needless to say, that all of it is conveniently available in Scanpy, e.g., through `.paga`, `.paga`, `.draw_graph(adata, init_pos='paga')`, `.umap(adata, init_pos='paga')`.

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