Alex Jung

@alex_w_jung

Vrijeme pridruživanja: srpanj 2019.
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  1. proslijedio/la je Tweet
    22. sij

    Excited to share our latest paper, which came out in early January "Clustered CTCF binding is an evolutionary mechanism to maintain topologically associating domains"!

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  2. proslijedio/la je Tweet
    30. stu 2019.

    Like the classic competitions that accelerated the era of deep learning, the Human Protein Atlas competition had 2,171 team compete for analyzing ~78,000 images--> high performance models and collaborators

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  3. proslijedio/la je Tweet
    22. stu 2019.

    Or it identifies a signature of somatic hypermutation producing clustered mutations with a distinct mutation spectrum at transcription start sites in lymphoid neoplasms. This appears to be the signature of AID and is different from the genome-wide clustered mutation spectrum 4/5

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  4. proslijedio/la je Tweet
    22. stu 2019.

    Want to learn mutational signatures jointly based on mutation types + genomic activity patterns? My student has developed TensorSignatures to better characterise and localise mutational processes. 1/5

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  5. proslijedio/la je Tweet
    6. stu 2019.

    My EMBL 2nd year PhD friends/fellows are doing a great job organizing this years' PhD Symposium, check it out! ;)

    All the organisers are currently at in Cambridge for a bioinformatic course. We also have brainstorming sessions to make sure that everything would be top-notch before November 28th!
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  6. proslijedio/la je Tweet
    5. stu 2019.

    For those at . Check out ’s poster 147 today. How to learn mutational signatures from mutation spectra *and* genomic activity patterns with TensorSignatures.

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

    It also finds a lot of associations in bulk transcriptome data, deconvolves the signal to find areas on each slide corresponding to molecular cell types such as tumour infiltrating lymphocytes. Entirely automated. 3/5

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  8. proslijedio/la je Tweet
    25. lis 2019.

    Similarly the network finds prognostic associations in most cancer types that match and augment conventional grading and subtyping and points out prognostically relevant regions, such as necrosis, on each slide. 4/5

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  9. proslijedio/la je Tweet
    25. lis 2019.

    This demonstrates how deep learning can integrate histopathology and genomics with huge potential for digitally augmenting diagnostic workflows - inc. challenges to tame a CNN. Great work by many lab members, Alex Jung and . Facilitated by and . 5/5 ///

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  10. proslijedio/la je Tweet
    25. lis 2019.

    Hello world. Here’s something interesting: from my lab trained a deep convolutional neural net in cancer histopathology *and* genomics using 14M images from 17k H&E slides across 28 cancer types. The outcome is stunning. 1/5

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  11. proslijedio/la je Tweet
    26. lis 2019.

    Without spatial annotation of tumor lymphocyte, our method PC-CHiP (Pan-Cancer Computational HistoPathology) is able to automatically identify regions with lymphocytes for thousands of large H&E whole tissue slides. Great tool to assist pathologists!

    It also finds a lot of associations in bulk transcriptome data, deconvolves the signal to find areas on each slide corresponding to molecular cell types such as tumour infiltrating lymphocytes. Entirely automated. 3/5
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