[Colloquium] REMINDER: 9/16 TTIC Colloquium: Rick Stevens, University of Chicago/Argonne National Laboratory

[Mary Marre]

*TTIC Colloquium*

[image: image.png]

*When:*      Monday, September 16th at 11:00 am



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



*Who: *       Rick Stevens,  Argonne National Laboratory / University of
Chicago



*Title: *       Deep Learning in Cancer Drug Response Prediction

*Abstract:* Artificial intelligence and machine learning (ML) specifically
is having an increasingly significant impact on our lives. Since the early
wins in computer vision from deep learning (DL) in the 2010’s, deep neural
networks have increasingly been applied to hard problems that have defied
previous modeling efforts. This is particularly true in chemistry and drug
development where there are dozens of efforts to replace the traditional
drug development computational pipelines with machine learning based
alternatives. In cancer drug development and predictive oncology there are
several cases where DL is beginning to show significant successes. In our
work we are applying deep learning to the problem of predicting tumor drug
response for both single drugs and drug combinations. We have developed
drug response models for cell lines, patient derived xenograft (PDX) models
and organdies that are used in preclinical drug development. Due to the
limited scale of available PDX data we have focused on transfer learning
approaches to generalize response prediction across biological model types.
We incorporate uncertainty quantification into our models to enable us to
determine the confidence interval of predictions. Our current approaches
leverage work on attention, weight sharing between closely related runs for
accelerated training and active learning for prioritization of experiments.
Our goal is a broad set of models that can be used to screen drugs during
early stage drug development as well as predicting tumor response for
pre-clinical study design. Results to date include response classifications
that achieve >92% balanced classification accuracy on a pan-cancer
collection of tumor models and broad collection of drugs. Our work is part
of a joint program of investment from the NCI and DOE and is supported in
part by the US Exascale Computing Project via the CANDLE project.

*Biography: *Professor Rick Stevens is internationally known for work in
high-performance computing, collaboration and visualization technology, and
for building computational tools and web infrastructures to support
large-scale genome and metagenome analysis for basic science and infectious
disease research. A current focus is the national initiatives for Exascale
computing and Artificial Intelligence (AI). He is the Associate Laboratory
Director at Argonne National Laboratory, and a Professor of Computer
Science at the University of Chicago. In addition, he is the principle
investigator of the NIH-NIAID funded PATRIC Bioinformatics Resource Center,
the Exascale Computing Project (ECP) Exascale Deep Learning and Simulation
Enabled Precision Medicine for Cancer project, and the predictive models
pilot of the DOE-NCI funded Joint Design of Advanced Computing Solutions
for Cancer (JDACS4C) project. Over the past twenty years, he and his
colleagues have developed the SEED, RAST, MG-RAST, and ModelSEED genome
analysis and bacterial modeling servers that have been used by tens of
thousands of users to annotate and analyze more than 250,000 microbial
genomes and metagenomic samples.


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 Sun, Sep 15, 2019 at 7:11 PM Mary Marre <mmarre at ttic.edu> wrote:

> *TTIC Colloquium*
>
> [image: image.png]
>
> *When:*      Monday, September 16th at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *       Rick Stevens,  Argonne National Laboratory / University of
> Chicago
>
>
>
> *Title: *       Deep Learning in Cancer Drug Response Prediction
>
> *Abstract:* Artificial intelligence and machine learning (ML)
> specifically is having an increasingly significant impact on our lives.
> Since the early wins in computer vision from deep learning (DL) in the
> 2010’s, deep neural networks have increasingly been applied to hard
> problems that have defied previous modeling efforts. This is particularly
> true in chemistry and drug development where there are dozens of efforts to
> replace the traditional drug development computational pipelines with
> machine learning based alternatives. In cancer drug development and
> predictive oncology there are several cases where DL is beginning to show
> significant successes. In our work we are applying deep learning to the
> problem of predicting tumor drug response for both single drugs and drug
> combinations. We have developed drug response models for cell lines,
> patient derived xenograft (PDX) models and organdies that are used in
> preclinical drug development. Due to the limited scale of available PDX
> data we have focused on transfer learning approaches to generalize response
> prediction across biological model types. We incorporate uncertainty
> quantification into our models to enable us to determine the confidence
> interval of predictions. Our current approaches leverage work on attention,
> weight sharing between closely related runs for accelerated training and
> active learning for prioritization of experiments. Our goal is a broad set
> of models that can be used to screen drugs during early stage drug
> development as well as predicting tumor response for pre-clinical study
> design. Results to date include response classifications that achieve >92%
> balanced classification accuracy on a pan-cancer collection of tumor models
> and broad collection of drugs. Our work is part of a joint program of
> investment from the NCI and DOE and is supported in part by the US Exascale
> Computing Project via the CANDLE project.
>
> *Biography: *Professor Rick Stevens is internationally known for work in
> high-performance computing, collaboration and visualization technology, and
> for building computational tools and web infrastructures to support
> large-scale genome and metagenome analysis for basic science and infectious
> disease research. A current focus is the national initiatives for Exascale
> computing and Artificial Intelligence (AI). He is the Associate Laboratory
> Director at Argonne National Laboratory, and a Professor of Computer
> Science at the University of Chicago. In addition, he is the principle
> investigator of the NIH-NIAID funded PATRIC Bioinformatics Resource Center,
> the Exascale Computing Project (ECP) Exascale Deep Learning and Simulation
> Enabled Precision Medicine for Cancer project, and the predictive models
> pilot of the DOE-NCI funded Joint Design of Advanced Computing Solutions
> for Cancer (JDACS4C) project. Over the past twenty years, he and his
> colleagues have developed the SEED, RAST, MG-RAST, and ModelSEED genome
> analysis and bacterial modeling servers that have been used by tens of
> thousands of users to annotate and analyze more than 250,000 microbial
> genomes and metagenomic samples.
>
>
> 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 Wed, Sep 11, 2019 at 1:01 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *TTIC Colloquium*
>>
>> [image: image.png]
>>
>> *When:*      Monday, September 16th at 11:00 am
>>
>>
>>
>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>>
>>
>> *Who: *       Rick Stevens,  Argonne National Laboratory / University of
>> Chicago
>>
>>
>>
>> *Title: *       Deep Learning in Cancer Drug Response Prediction
>>
>> *Abstract:* Artificial intelligence and machine learning (ML)
>> specifically is having an increasingly significant impact on our lives.
>> Since the early wins in computer vision from deep learning (DL) in the
>> 2010’s, deep neural networks have increasingly been applied to hard
>> problems that have defied previous modeling efforts. This is particularly
>> true in chemistry and drug development where there are dozens of efforts to
>> replace the traditional drug development computational pipelines with
>> machine learning based alternatives. In cancer drug development and
>> predictive oncology there are several cases where DL is beginning to show
>> significant successes. In our work we are applying deep learning to the
>> problem of predicting tumor drug response for both single drugs and drug
>> combinations. We have developed drug response models for cell lines,
>> patient derived xenograft (PDX) models and organdies that are used in
>> preclinical drug development. Due to the limited scale of available PDX
>> data we have focused on transfer learning approaches to generalize response
>> prediction across biological model types. We incorporate uncertainty
>> quantification into our models to enable us to determine the confidence
>> interval of predictions. Our current approaches leverage work on attention,
>> weight sharing between closely related runs for accelerated training and
>> active learning for prioritization of experiments. Our goal is a broad set
>> of models that can be used to screen drugs during early stage drug
>> development as well as predicting tumor response for pre-clinical study
>> design. Results to date include response classifications that achieve >92%
>> balanced classification accuracy on a pan-cancer collection of tumor models
>> and broad collection of drugs. Our work is part of a joint program of
>> investment from the NCI and DOE and is supported in part by the US Exascale
>> Computing Project via the CANDLE project.
>>
>> *Biography: *Professor Rick Stevens is internationally known for work in
>> high-performance computing, collaboration and visualization technology, and
>> for building computational tools and web infrastructures to support
>> large-scale genome and metagenome analysis for basic science and infectious
>> disease research. A current focus is the national initiatives for Exascale
>> computing and Artificial Intelligence (AI). He is the Associate Laboratory
>> Director at Argonne National Laboratory, and a Professor of Computer
>> Science at the University of Chicago. In addition, he is the principle
>> investigator of the NIH-NIAID funded PATRIC Bioinformatics Resource Center,
>> the Exascale Computing Project (ECP) Exascale Deep Learning and Simulation
>> Enabled Precision Medicine for Cancer project, and the predictive models
>> pilot of the DOE-NCI funded Joint Design of Advanced Computing Solutions
>> for Cancer (JDACS4C) project. Over the past twenty years, he and his
>> colleagues have developed the SEED, RAST, MG-RAST, and ModelSEED genome
>> analysis and bacterial modeling servers that have been used by tens of
>> thousands of users to annotate and analyze more than 250,000 microbial
>> genomes and metagenomic samples.
>>
>>
>> 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 Wed, Sep 11, 2019 at 10:38 AM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *TTIC Colloquium*
>>>
>>> [image: image.png]
>>>
>>> *When:*      Monday, September 16th at 11:00 am
>>>
>>>
>>>
>>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>>
>>>
>>>
>>> *Who: *       Rick Stevens, University of Chicago / Argonne National
>>> Laboratory
>>>
>>>
>>>
>>> *Title: *       Deep Learning for Precision Medicine for Cancer
>>>
>>> *Abstract:*  tba
>>>
>>> *Biography: *Professor Rick Stevens is internationally known for work
>>> in high-performance computing, collaboration and visualization technology,
>>> and for building computational tools and web infrastructures to support
>>> large-scale genome and metagenome analysis for basic science and infectious
>>> disease research. A current focus is the national initiatives for Exascale
>>> computing and Artificial Intelligence (AI). He is the Associate Laboratory
>>> Director at Argonne National Laboratory, and a Professor of Computer
>>> Science at the University of Chicago. In addition, he is the principle
>>> investigator of the NIH-NIAID funded PATRIC Bioinformatics Resource Center,
>>> the Exascale Computing Project (ECP) Exascale Deep Learning and Simulation
>>> Enabled Precision Medicine for Cancer project, and the predicitive models
>>> pilot of the DOE-NCI funded Joint Design of Advanced Computing Solutions
>>> for Cancer (JDACS4C) project. Over the past twenty years, he and his
>>> colleagues have developed the SEED, RAST, MG-RAST, and ModelSEED genome
>>> analysis and bacterial modeling servers that have been used by tens of
>>> thousands of users to annotate and analyze more than 250,000 microbial
>>> genomes and metagenomic samples.
>>>
>>>
>>> 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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