Re: [AIRG] Neural networks for simulating biological cells, 03/06, CS 3310

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

Today Matt Bernstein will be talking to us about how to model a cell 
using deep networks and what can be discovered by doing so when the 
structure of the network reflects the structure of the problem.

4pm, CS 3310
https://www.nature.com/articles/nmeth.4627

Hope to see you there!

Aubrey


On 3/1/19 12:02 PM, MATTHEW NATHAN BERNSTEIN via AIRG wrote:
> Hi AIRG,
> 
> Next week I will talk about a recent tool for simulating a biological 
> cell using neural networks.
> 
> *Title*: Using deep learning to model the hierarchical structure and 
> function of a cell
> *Authors*: Jianzhu Ma and others â
> *Paper*: https://www.nature.com/articles/nmeth.4627
> *Presenter*: Matt Bernstein
> *âTime*: 4pm - March 6, 2019â
> *Place*: CS 3310
> 
> *Summary*:â
> 
> We tend not to think of neural networks as being easy to interpret; 
> however, when the structure of the neural network matches the 
> specifications of the problems, neural networks can be powerful tools 
> for understanding the structure of the data. This has been most evident 
> with convolutional neural networks trained on image data, which learn a 
> hierarchy of features for making predictions on images. This week, I 
> will discuss a project that builds neural networks for which their 
> architecture is based on the structure of known biological pathways and 
> subsystems in a cell. The authors show that when they train these 
> neural networks to predict the behavior of the cell, they are able to 
> examine the activation of neurons in the network to gain insight into 
> the machinery of the cell. I think this work is interesting because it 
> provides a compelling case of how combining prior knowledge with neural 
> networks can produce powerful tools for not only making predictions, but 
> also gaining novel insights.
> 
> 
> 
> 
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