[Theory] REMINDER: 2/18 Thesis Defense: Siqi Sun, TTIC

[Mary Marre]

*Thesis Defense: Siqi Sun, TTIC*

*When:*      Tuesday, February 18th  at 1:30pm

*Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 501

*Who: *       Siqi Sun, TTIC


*Title:   *     Unsupervised and Supervised Structure Learning for Protein
Contact Prediction

*Abstract:*
Protein contacts provide key information for the understanding of protein
structure and function, and therefore contact prediction from sequences is
an important problem. Recent research shows that some correctly predicted
long-range contacts could help topology-level structure modeling. Thus,
contact prediction and contact-assisted protein folding also proves the
importance of this problem. In this thesis, I will briefly introduce the
extant related work, then show how to establish the contact prediction
through unsupervised graphical models with topology constraints. Further, I
will explain how to use the supervised deep learning methods to further
boost the accuracy of contact prediction. Finally, I will propose a scoring
system called diversity score to measure the novelty of contact
predictions, as well as an algorithm that predicts contacts with respect to
the new scoring system.


*Thesis advisor:* Jinbo Xu <j3xu at ttic.edu>


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 Tue, Feb 11, 2020 at 5:23 PM Mary Marre <mmarre at ttic.edu> wrote:

> *Thesis Defense: Siqi Sun, TTIC*
>
> *When:*      Tuesday, February 18th  at 1:30pm
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 501
>
> *Who: *       Siqi Sun, TTIC
>
>
> *Title:   *     Unsupervised and Supervised Structure Learning for
> Protein Contact Prediction
>
> *Abstract:*
> Protein contacts provide key information for the understanding of protein
> structure and function, and therefore contact prediction from sequences is
> an important problem. Recent research shows that some correctly predicted
> long-range contacts could help topology-level structure modeling. Thus,
> contact prediction and contact-assisted protein folding also proves the
> importance of this problem. In this thesis, I will briefly introduce the
> extant related work, then show how to establish the contact prediction
> through unsupervised graphical models with topology constraints. Further, I
> will explain how to use the supervised deep learning methods to further
> boost the accuracy of contact prediction. Finally, I will propose a scoring
> system called diversity score to measure the novelty of contact
> predictions, as well as an algorithm that predicts contacts with respect to
> the new scoring system.
>
>
> *Thesis advisor:* Jinbo Xu <j3xu at ttic.edu>
>
>
>
> 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 Tue, Feb 4, 2020 at 12:38 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *Thesis Defense: Siqi Sun, TTIC*
>>
>> *When:*      Tuesday, February 18th  at 1:30pm
>>
>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 501
>>
>> *Who: *       Siqi Sun, TTIC
>>
>>
>> *Title:   *     Unsupervised and Supervised Structure Learning for
>> Protein Contact Prediction
>>
>> *Abstract:*
>> Protein contacts provide key information for the understanding of protein
>> structure and function, and therefore contact prediction from sequences is
>> an important problem. Recent research shows that some correctly predicted
>> long-range contacts could help topology-level structure modeling. Thus,
>> contact prediction and contact-assisted protein folding also proves the
>> importance of this problem. In this thesis, I will briefly introduce the
>> extant related work, then show how to establish the contact prediction
>> through unsupervised graphical models with topology constraints. Further, I
>> will explain how to use the supervised deep learning methods to further
>> boost the accuracy of contact prediction. Finally, I will propose a scoring
>> system called diversity score to measure the novelty of contact
>> predictions, as well as an algorithm that predicts contacts with respect to
>> the new scoring system.
>>
>>
>> *Thesis advisor:* Jinbo Xu <j3xu at ttic.edu>
>>
>>
>>
>>
>> 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>*
>>
>
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