Jinbo Xu

@jinboxu_chicago

Interested in Machine Learning, Optimization and Bioinformatics

Vrijeme pridruživanja: veljača 2017.

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  1. 11. stu 2019.

    Just saw the video of my talk at ISMB 3DSIG in the summer of 2019 about deep learning for protein structure prediction. In case that you are interested in it, it is at .

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    26. kol 2019.

    structures could be accurately predicted with deep learning on personal computers. A groundbreaking discovery, this finding could be the key to understanding processes. In PNAS:

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  3. 22. kol 2019.
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  4. 22. kol 2019.

    There will be a computational biology workshop in my institute in September 2019. Registration is free and all are welcome to attend. See for some details.

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  5. 22. kol 2019.

    A recent comprehensive review of machine learning for protein structure prediction (with some minor inaccuracy) Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning | Journal of Computational Biology

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  6. 21. kol 2019.

    Impact factor impacts on early-career scientist careers

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    20. kol 2019.

    Read highlights from this week’s issue of PNAS: Tracking the source of radioactive ruthenium, Deep learning and protein structure prediction, Neonicotinoids, honeydew, and insect mortality:

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  8. 20. kol 2019.

    An excellent review on protein structure prediction and design.

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  9. 12. kol 2019.

    The journal version of my paper about deep learning for distance-based folding is officially published. Some of my source code is also released along with this publication. Distance-based protein folding powered by deep learning

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  10. 10. srp 2019.

    Are Commercial Labs Stealing Academia’s AI Thunder? by

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  11. 24. svi 2019.

    Not sure if this works or not. Readers will find out the journal name sooner or later.

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  12. 24. svi 2019.

    Had a great time while visiting Baker’s group. Also learned a lot from his young people.

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    Open source software is 🔑 to science. Many of the packages, libraries + applications crucial to biomedicine are built by researchers who volunteer their time + effort to make these tools available. We’re excited to announce support for tools

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  14. 6. svi 2019.
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  15. 2. svi 2019.

    Just finished a report of our CASP13 results: . Surprised to find out that we predicted correct folds for all the 3 largest hard targets (~350 amino acids) and generated the best 3D models for two of them (T0950-D1 and T0969-D1).

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