[Colloquium] Re: Reminder: 2/1 Talks at TTIC: Wei Zhang, University of Minnesota

[Mary Marre via Colloquium]

When:     Wednesday, February 1st at 11:00 am

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

Who:       Wei Zhang, University of Minnesota


Title: Computational Analysis of Transcript Interactions and Variants in
Cancer


Abstract: Gene expression and gene isoforms in cancer transcriptome are
informative for phenotype prediction. Network-based learning models are
playing increasing role in cancer transcriptome analysis. These methods
integrate large scale patient transcriptome data with structural
information in biological networks to improve phenotype prediction
accuracy, model robustness and biological interpretation of results. In
this talk, I will present two such reliable network-based methods.  First,
I will introduce a Network-based method for RNA-Seq-based Transcript
Quantification (Net-RSTQ), which integrates protein domain-domain
interaction information with RNA-Seq short read alignments for transcript
abundance estimation under the assumption that the abundances of the
neighboring transcripts by domain-domain interactions in transcript
interaction network are positively correlated. Second, I will present a
Network-based Cox regression model (Net-Cox), which integrates gene network
information into the Cox’s proportional hazard model to explore the
co-expression or functional relation among high-dimensional gene expression
features in a gene network. In the experiments of studying the cancer
trasnscriptome data in The Cancer Genome Atlas (TCGA), it was observed that
both models can improve cancer treatment outcome prediction. Throughout the
talk, I will also talk about my future research direction.


Bio: Dr. Wei Zhang is a Research Associate in Computer Science and
Engineering at the University of Minnesota Twin Cities. His research
interests include computational biology and machine learning. His research
has centered around developing network-based algorithms to better
understand three specific topics in cancer transcriptome: alternative
splicing, alternative polyadenylation and interactions. He received his PhD
and MS from University of Minnesota Twin Cities in 2015 and 2011, BS from
Hebei University of Technology in 2009, all in computer science.



Host: Jinbo Xu <j3xu at ttic.edu>


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, Jan 31, 2017 at 2:32 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Wednesday, February 1st at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Wei Zhang, University of Minnesota
>
>
> Title: Computational Analysis of Transcript Interactions and Variants in
> Cancer
>
>
> Abstract: Gene expression and gene isoforms in cancer transcriptome are
> informative for phenotype prediction. Network-based learning models are
> playing increasing role in cancer transcriptome analysis. These methods
> integrate large scale patient transcriptome data with structural
> information in biological networks to improve phenotype prediction
> accuracy, model robustness and biological interpretation of results. In
> this talk, I will present two such reliable network-based methods.  First,
> I will introduce a Network-based method for RNA-Seq-based Transcript
> Quantification (Net-RSTQ), which integrates protein domain-domain
> interaction information with RNA-Seq short read alignments for transcript
> abundance estimation under the assumption that the abundances of the
> neighboring transcripts by domain-domain interactions in transcript
> interaction network are positively correlated. Second, I will present a
> Network-based Cox regression model (Net-Cox), which integrates gene network
> information into the Cox’s proportional hazard model to explore the
> co-expression or functional relation among high-dimensional gene expression
> features in a gene network. In the experiments of studying the cancer
> trasnscriptome data in The Cancer Genome Atlas (TCGA), it was observed that
> both models can improve cancer treatment outcome prediction. Throughout the
> talk, I will also talk about my future research direction.
>
>
> Bio: Dr. Wei Zhang is a Research Associate in Computer Science and
> Engineering at the University of Minnesota Twin Cities. His research
> interests include computational biology and machine learning. His research
> has centered around developing network-based algorithms to better
> understand three specific topics in cancer transcriptome: alternative
> splicing, alternative polyadenylation and interactions. He received his PhD
> and MS from University of Minnesota Twin Cities in 2015 and 2011, BS from
> Hebei University of Technology in 2009, all in computer science.
>
>
>
> Host: Jinbo Xu <j3xu at ttic.edu>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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