[Colloquium] REMINDER: 3/7 Talks at TTIC: Peter Koo, Harvard University

[Mary Marre]

When:     Thursday, March 7th at *11:00 am*

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Peter Koo, Harvard University


*Title: *      Interpretable Deep Learning for Biological Sequence Analysis
*Abstract:* Deep learning methods have the potential to make a significant
impact in biology and healthcare, but a major challenge is understanding
the reasons behind their predictions. In this talk, I will demonstrate how
interpreting these “black box” models can: 1) provide novel biological
insights and 2) help navigate better model design for big, noisy biological
sequence data. In the first part of the talk, I will present results from
interrogating a convolutional neural network (CNN) trained to infer
sequence and RNA structure specificities of RNA-binding proteins. We find
that in addition to sequence motifs, our CNN learns a model that considers
the number of motifs, their spacing, and both positive and negative effects
of RNA structure context. In the second part of the talk, I will discuss
ongoing research which demonstrates how deep learning can help design
better models for protein contact predictions. Specifically, we interpret a
variational autoencoder (VAE) that is trained on aligned, homologous
protein sequences. We find that our VAEs capture phylogenetic relationships
with an approximate Bayesian mixture model of profiles, *i.e.* site-independent
amino-acid probability models, a result that serves as a good null model
for contact predictions. By using our model as a new background correction
method, we show that mutual information provides significantly improved
contact predictions while remaining more scalable than alternative methods.

Host: Jinbo Xu <j3xu at ttic.edu>

Mary C. Marre
Administrative Assistant
*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>*

Mary C. Marre
Administrative Assistant
*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 Wed, Mar 6, 2019 at 4:30 PM Mary Marre <mmarre at ttic.edu> wrote:

> When:     Thursday, March 7th at *11:00 am*
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Peter Koo, Harvard University
>
>
> *Title: *      Interpretable Deep Learning for Biological Sequence
> Analysis
> *Abstract:* Deep learning methods have the potential to make a
> significant impact in biology and healthcare, but a major challenge is
> understanding the reasons behind their predictions. In this talk, I will
> demonstrate how interpreting these “black box” models can: 1) provide novel
> biological insights and 2) help navigate better model design for big, noisy
> biological sequence data. In the first part of the talk, I will present
> results from interrogating a convolutional neural network (CNN) trained to
> infer sequence and RNA structure specificities of RNA-binding proteins. We
> find that in addition to sequence motifs, our CNN learns a model that
> considers the number of motifs, their spacing, and both positive and
> negative effects of RNA structure context. In the second part of the talk,
> I will discuss ongoing research which demonstrates how deep learning can
> help design better models for protein contact predictions. Specifically, we
> interpret a variational autoencoder (VAE) that is trained on aligned,
> homologous protein sequences. We find that our VAEs capture phylogenetic
> relationships with an approximate Bayesian mixture model of profiles,
> *i.e.* site-independent amino-acid probability models, a result that
> serves as a good null model for contact predictions. By using our model as
> a new background correction method, we show that mutual information
> provides significantly improved contact predictions while remaining more
> scalable than alternative methods.
>
> Host: Jinbo Xu <j3xu at ttic.edu>
>
> Mary C. Marre
> Administrative Assistant
> *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, Mar 1, 2019 at 1:39 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Thursday, March 7th at *11:00 am*
>>
>> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Peter Koo, Harvard University
>>
>>
>> *Title: *      Interpretable Deep Learning for Biological Sequence
>> Analysis
>> *Abstract:* Deep learning methods have the potential to make a
>> significant impact in biology and healthcare, but a major challenge is
>> understanding the reasons behind their predictions. In this talk, I will
>> demonstrate how interpreting these “black box” models can: 1) provide novel
>> biological insights and 2) help navigate better model design for big, noisy
>> biological sequence data. In the first part of the talk, I will present
>> results from interrogating a convolutional neural network (CNN) trained to
>> infer sequence and RNA structure specificities of RNA-binding proteins. We
>> find that in addition to sequence motifs, our CNN learns a model that
>> considers the number of motifs, their spacing, and both positive and
>> negative effects of RNA structure context. In the second part of the talk,
>> I will discuss ongoing research which demonstrates how deep learning can
>> help design better models for protein contact predictions. Specifically, we
>> interpret a variational autoencoder (VAE) that is trained on aligned,
>> homologous protein sequences. We find that our VAEs capture phylogenetic
>> relationships with an approximate Bayesian mixture model of profiles,
>> *i.e.* site-independent amino-acid probability models, a result that
>> serves as a good null model for contact predictions. By using our model as
>> a new background correction method, we show that mutual information
>> provides significantly improved contact predictions while remaining more
>> scalable than alternative methods.
>>
>> Host: Jinbo Xu <j3xu at ttic.edu>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *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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