[Theory] TODAY!!! 4/6 Talks at TTIC: Yu Li, KAUST
Mary Marre
mmarre at ttic.edu
Mon Apr 6 10:28:39 CDT 2020
*PLEASE NOTE: UPDATED ZOOM LINK*
*When:* Monday, April 6th at 11:00 am
*Where:* Zoom Virtual Talk (see details below)
*Who: * Yu Li, KAUST
*Title: *Towards Understanding Biomolecular Structure and Function
with Deep Learning
*Abstract: *Biomolecules, existing in high-order structural forms, are
indispensable for the normal functioning of our bodies. To demystify those
critical biological processes, we need to investigate biomolecular
structures and functions. In this talk, we showcase our efforts in that
research direction using deep learning. First, we proposed a deep learning
guarded Bayesian inference framework for reconstructing super-resolved
structure images from the super-resolved fluorescence microscopy data. This
framework enables us to observe the overall biomolecular structures in
living cells with super-resolution in almost real-time. Then, we zoom in on
a particular biomolecule, RNA, predicting its secondary structure. For this
one of the oldest problems in bioinformatics, we proposed an unrolled deep
learning method, which can bring us with 20% performance improvement,
regarding the F1 score. Finally, by leveraging the physiochemical features
and deep learning, we proposed the first-of-its-kind framework to
investigate the interaction between RNA and RNA-binding proteins (RBP).
This framework can provide us with both the interaction details and
high-throughput binding prediction results. Extensive *in vitro* and *in
vivo *biological experiments demonstrate the effectiveness of the proposed
method.
*Bio:* Yu Li is a PhD student at KAUST in Saudi Arabia, majoring in
Computer Science, under the supervision of Prof. Xin Gao. He is a member of
Computational Bioscience Research Center (CBRC) at KAUST. His main research
interest is developing novel and new machine learning methods, mainly deep
learning methods, for solving the computational problems in biology and
understanding the principles behind the bio-world. He obtained an MS degree
in CS from KAUST in 2016. Before that, he got the Bachelor degree in
Biosciences from University of Science and Technology of China (USTC).
*Host:* Jinbo Xu <j3xu at ttic.edu>
******************************************************************
Jinbo Xu is inviting you to a scheduled Zoom meeting.
Topic: 4/6 Talks at TTIC: Yu Li, KAUST
Time: Apr 6, 2020 11:00 AM Central Time (US and Canada)
Join Zoom Meeting
*https://zoom.us/j/521305243 <https://zoom.us/j/521305243>*
Meeting ID: 521 305 243
*Password: 343272*
One tap mobile
+13462487799,,521305243# US (Houston)
+14086380968,,521305243# US (San Jose)
Dial by your location
+1 346 248 7799 US (Houston)
+1 408 638 0968 US (San Jose)
+1 646 876 9923 US (New York)
+1 669 900 6833 US (San Jose)
+1 253 215 8782 US
+1 301 715 8592 US
+1 312 626 6799 US (Chicago)
Meeting ID: 521 305 243
Password: 343272
Find your local number: https://zoom.us/u/akrIgOS2N
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL 60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
On Sun, Apr 5, 2020 at 7:33 PM Mary Marre <mmarre at ttic.edu> wrote:
> *PLEASE NOTE: UPDATED ZOOM LINK*
>
>
> *When:* Monday, April 6th at 11:00 am
>
>
>
> *Where:* Zoom Virtual Talk (see details below)
>
>
>
> *Who: * Yu Li, KAUST
>
>
>
> *Title: *Towards Understanding Biomolecular Structure and
> Function with Deep Learning
>
> *Abstract: *Biomolecules, existing in high-order structural forms, are
> indispensable for the normal functioning of our bodies. To demystify those
> critical biological processes, we need to investigate biomolecular
> structures and functions. In this talk, we showcase our efforts in that
> research direction using deep learning. First, we proposed a deep learning
> guarded Bayesian inference framework for reconstructing super-resolved
> structure images from the super-resolved fluorescence microscopy data. This
> framework enables us to observe the overall biomolecular structures in
> living cells with super-resolution in almost real-time. Then, we zoom in on
> a particular biomolecule, RNA, predicting its secondary structure. For this
> one of the oldest problems in bioinformatics, we proposed an unrolled deep
> learning method, which can bring us with 20% performance improvement,
> regarding the F1 score. Finally, by leveraging the physiochemical features
> and deep learning, we proposed the first-of-its-kind framework to
> investigate the interaction between RNA and RNA-binding proteins (RBP).
> This framework can provide us with both the interaction details and
> high-throughput binding prediction results. Extensive *in vitro* and *in
> vivo *biological experiments demonstrate the effectiveness of the
> proposed method.
>
> *Bio:* Yu Li is a PhD student at KAUST in Saudi Arabia, majoring in
> Computer Science, under the supervision of Prof. Xin Gao. He is a member of
> Computational Bioscience Research Center (CBRC) at KAUST. His main research
> interest is developing novel and new machine learning methods, mainly deep
> learning methods, for solving the computational problems in biology and
> understanding the principles behind the bio-world. He obtained an MS degree
> in CS from KAUST in 2016. Before that, he got the Bachelor degree in
> Biosciences from University of Science and Technology of China (USTC).
>
>
>
> *Host:* Jinbo Xu <j3xu at ttic.edu>
>
> ******************************************************************
> Jinbo Xu is inviting you to a scheduled Zoom meeting.
>
> Topic: 4/6 Talks at TTIC: Yu Li, KAUST
> Time: Apr 6, 2020 11:00 AM Central Time (US and Canada)
>
> Join Zoom Meeting
> *https://zoom.us/j/521305243 <https://zoom.us/j/521305243>*
>
> Meeting ID: 521 305 243
> *Password: 343272*
>
> One tap mobile
> +13462487799,,521305243# US (Houston)
> +14086380968,,521305243# US (San Jose)
>
> Dial by your location
> +1 346 248 7799 US (Houston)
> +1 408 638 0968 US (San Jose)
> +1 646 876 9923 US (New York)
> +1 669 900 6833 US (San Jose)
> +1 253 215 8782 US
> +1 301 715 8592 US
> +1 312 626 6799 US (Chicago)
> Meeting ID: 521 305 243
> Password: 343272
> Find your local number: https://zoom.us/u/akrIgOS2N
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL 60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Fri, Apr 3, 2020 at 10:44 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *PLEASE NOTE: UPDATED ZOOM LINK*
>>
>>
>> *When:* Monday, April 6th at 11:00 am
>>
>>
>>
>> *Where:* Zoom Virtual Talk (see details below)
>>
>>
>>
>> *Who: * Yu Li, KAUST
>>
>>
>>
>> *Title: *Towards Understanding Biomolecular Structure and
>> Function with Deep Learning
>>
>> *Abstract: *Biomolecules, existing in high-order structural forms, are
>> indispensable for the normal functioning of our bodies. To demystify those
>> critical biological processes, we need to investigate biomolecular
>> structures and functions. In this talk, we showcase our efforts in that
>> research direction using deep learning. First, we proposed a deep learning
>> guarded Bayesian inference framework for reconstructing super-resolved
>> structure images from the super-resolved fluorescence microscopy data. This
>> framework enables us to observe the overall biomolecular structures in
>> living cells with super-resolution in almost real-time. Then, we zoom in on
>> a particular biomolecule, RNA, predicting its secondary structure. For this
>> one of the oldest problems in bioinformatics, we proposed an unrolled deep
>> learning method, which can bring us with 20% performance improvement,
>> regarding the F1 score. Finally, by leveraging the physiochemical features
>> and deep learning, we proposed the first-of-its-kind framework to
>> investigate the interaction between RNA and RNA-binding proteins (RBP).
>> This framework can provide us with both the interaction details and
>> high-throughput binding prediction results. Extensive *in vitro* and *in
>> vivo *biological experiments demonstrate the effectiveness of the
>> proposed method.
>>
>> *Bio:* Yu Li is a PhD student at KAUST in Saudi Arabia, majoring in
>> Computer Science, under the supervision of Prof. Xin Gao. He is a member of
>> Computational Bioscience Research Center (CBRC) at KAUST. His main research
>> interest is developing novel and new machine learning methods, mainly deep
>> learning methods, for solving the computational problems in biology and
>> understanding the principles behind the bio-world. He obtained an MS degree
>> in CS from KAUST in 2016. Before that, he got the Bachelor degree in
>> Biosciences from University of Science and Technology of China (USTC).
>>
>>
>>
>> *Host:* Jinbo Xu <j3xu at ttic.edu>
>>
>>
>> *************************************************************************************************
>> Jinbo Xu is inviting you to a scheduled Zoom meeting.
>>
>> Topic: 4/6 Talks at TTIC: Yu Li, KAUST
>> Time: Apr 6, 2020 11:00 AM Central Time (US and Canada)
>>
>> Join Zoom Meeting
>> *https://zoom.us/j/521305243 <https://zoom.us/j/521305243>*
>>
>> Meeting ID: 521 305 243
>> *Password: 343272*
>>
>> One tap mobile
>> +13462487799,,521305243# US (Houston)
>> +14086380968,,521305243# US (San Jose)
>>
>> Dial by your location
>> +1 346 248 7799 US (Houston)
>> +1 408 638 0968 US (San Jose)
>> +1 646 876 9923 US (New York)
>> +1 669 900 6833 US (San Jose)
>> +1 253 215 8782 US
>> +1 301 715 8592 US
>> +1 312 626 6799 US (Chicago)
>> Meeting ID: 521 305 243
>> Password: 343272
>> Find your local number: https://zoom.us/u/akrIgOS2N
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 517*
>> *Chicago, IL 60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20200406/30e436e9/attachment-0001.html>
More information about the Theory
mailing list