Sean Maden

@MadenSean

Ph.D. candidate in Computational Biology at OHSU/Knight. Tools: Python, R, bash; Topics: Epigenomics, cancer, machine learning, cloud servers

Portland, OR
Vrijeme pridruživanja: lipanj 2018.

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  1. Prikvačeni tweet
    4. velj

    Pretty awesome that we already have a ton of genomic data on 2019-nCoV (Wuhan novel coronavirus), with something like 35 studies already published exploring the origins and nature of the virus. Modern medicine and public health in action!

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  2. proslijedio/la je Tweet

    Don't Panic! If you have ATAC-seq data and you're not sure of the best way to analyze it , and co have written a review to help you. You'll soon know where your towel is.

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  3. proslijedio/la je Tweet
    30. sij

    Thanks for the great summary and of course to our coauthors also. Excited to have this out, and especially to be the first paper in NAR Cancer!

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  4. proslijedio/la je Tweet
    30. sij

    1/7 Congrats to , , my co-conspirator and especially the 🌟 of the show for making the very first research paper in 's new subjournal NAR Cancer happen!

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  5. proslijedio/la je Tweet
    25. sij

    Simulation models can be a useful way to combine what we think we know into a numeric summary, but that summary is only as good as our knowledge. If we have very little real knowledge, like with the new coronavirus, the summary can be very very wrong. Be patient & wait for data.

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  6. proslijedio/la je Tweet
    24. sij

    Congrats to the many scientists and collaborators on our recent paper in . Big congrats to Emily Burke and Josh Chenoweth who led this effort! 1/5

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  7. proslijedio/la je Tweet
    10. lis 2018.

    Slides () and repo () for my Machine Learning in talk for are now live! ⭐️🤟I had so much fun presenting and preparing these - I hope these will be enjoyed!

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  8. 7. sij

    Quantifying dysfunctional biological aging in colon and CRC using multiple epigenetic clocks and aging models! Stoked to finally see this work in print, my latest co-authorship and collaboration with Grady Lab

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  9. proslijedio/la je Tweet
    18. pro 2019.

    Automated acquisition of explainable knowledge from unannotated histopathology images

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  10. proslijedio/la je Tweet
    14. pro 2019.

    Nice analysis of replicability of DNA methylation/transcriptional studies. Lots of cell subtype and other effects found. Guessing DNA sequence will account for a major proportion of remaining variation. These aren’t confounders. These are results.

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  11. 14. pro 2019.
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  12. proslijedio/la je Tweet
    23. stu 2019.

    Little known fact — uses nanosnails to make its beadarrays

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  13. 21. stu 2019.

    Also very emphatic thanks to and the rest of the organizers for all their work coordinating this event in beautiful Banff, AB... bravo!

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  14. 21. stu 2019.

    !!! :)

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  15. 21. stu 2019.

    Amazing time at @CanEpi2019! So much great epigenetics research and biological insight. Thanks to those who gave me feedback on recount methylation, esp , ! Special thanks to , for making this possible!

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  16. proslijedio/la je Tweet
    20. stu 2019.

    Joe Ecker showing evidence that epigenetically your brain is not mature until age 25 — how did the car rental companies know??

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  17. proslijedio/la je Tweet
    19. stu 2019.

    Thanks to these emerging leaders in for forming today’s panel on Challenges & Opportunities in Epigenetic Studies , and to ’s SinéadAherne for organising everything

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  18. proslijedio/la je Tweet
    19. stu 2019.

    Sheila Tevez showing her great work on mitotic bookmarking, showing her published work that overturned prior paradigms that ended up being based on fixation artefacts. Oops.

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  19. proslijedio/la je Tweet
    18. stu 2019.

    LeAnn Howe presenting her work on histone acetylation, lovely provocative work on cause versus consequence in transcriptional regulation The majority of histone acetylation is a consequence of transcription

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  20. 18. stu 2019.

    Awesome talk! Highlights include promoter "keyhole" phenomenon, or 1-nt precise, specific, and replicable gene repression. Goes against probabilistic and imprecise TF promoter interactions.

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