Friday, May 20, 2022

A new document has been shared with you on 5/20/2022 3:29:37 p.m. - LPS


  Adobe  


            PDF

Scanned document.pdf


A new file has been shared with your email
glabstracts.mbglab@blogger.com on 5/20/2022 3:29:37 p.m.








Rev: 353836484639
Shared to glabstracts.mbglab@blogger.com
Cloud service



Wednesday, March 16, 2022

Fwd: AACR Annual Meeting 2022-session information needed

Session Title:

Functional Role of Long Tail Coding and Non-coding Mutations in Cancer

Session Abstract:

This session will focus on the role of non-coding mutations in cancer.
As most know, the overwhelming part of the genome is noncoding, and
there are many somatic mutations in cancer in this region. It is still
debated the degree to which many of these mutations are under positive
selection and whether these mutations could represent either strong or
weak drivers and affect cancer progression. This session will tackle
these issues.

==
i0aacr22

Friday, February 25, 2022

Fwd: Mechanick Lecture at UMD

Neurogenomics and 2 topics inspired by this, Quantum Computing & Privacy

Abstract:

My seminar will discuss various data-science issues related to
neurogenomics. First, I will focus on classic disorders of the brain,
which affect nearly a fifth of the world's population. Robust
phenotype-genotype associations have been established for several
psychiatric diseases (e.g., schizophrenia and bipolar disorder). However,
understanding their molecular causes is still a challenge. To address
this, the PsychENCODE consortium generated thousands of transcriptome
(bulk and single-cell) datasets from nearly 2000 individuals. Using these
data, we have developed interpretable machine learning approaches for
deciphering functional genomic elements and linkages in the brain and
psychiatric disorders. Specifically, we developed a deep-learning
model embedding the physical regulatory network to predict phenotype
from genotype. Our model uses a conditional Deep Boltzmann Machine
architecture and introduces lateral connectivity at the visible layer
to embed the biological structure learned from the regulatory network
and QTL linkages. Our model improves disease prediction (6X compared
to additive polygenic risk scores), highlights key genes for
disorders, and imputes missing transcriptome information from genotype
data alone. Next, I will look at the "data exhaust" from this activity
- that is, how one can find other things from the genomic analyses
than what is necessarily intended. I will focus on genomic privacy,
which is a main stumbling block in large-scale
genomic studies. In particular, I will look at how the quantifications
of expression levels can reveal something about the subjects studied
and how one can take steps to sanitize the data and protect patient
anonymity. Lastly, I will look at how the kinds of interpretable
deep-learning models we have developed can be extended to incorporate
Quantum Machine Learning approaches. We have developed Quantum
Boltzmann machines and Quantum Neural Network analogues of the
architectures from the first part of the talk, which we test using
simulations and implementations on quantum hardware. We show hints
that such models may outperform matched classical models on certain
genomics tasks, and show how parameters unique to the quantum versions
of the models can be interpreted in the context of gene regulation
networks.

==
i0umd22

Sunday, February 13, 2022

connecting

Hi there,


I'm Alison from the Juni Learning editorial team. We teach children how to code in a fun, relaxed atmosphere and provide insights to kids, parents, and programmers.


Your page caught my attention as I was going through study of biology, biology classes/ learning/ course/ subject/ lesson/ lecture, biology blog-related content and came across http://abstracts.gersteinlab.org/2014/11/invited-talk-at-2014-nips-workshop-on.html.


It seems we cover similar topics, so I was wondering if it can be linked to our piece Explore 50+ Online Courses for Kids - Intro to Biology https://junilearning.com/courses/human-biology-for-kids-private.


Is this something you can add to any of your articles? In exchange, our team will be happy to promote any links from your end, through our Twitter accounts. That way, we help grow each other's audience. 


Would love to know your thoughts, let me know if this is something we can work on. Looking forward to hearing from you!


Cheers,



Alison Clarke

Content Specialist


     

*If you do not wish to receive an email from us in the future, please let me know, and I'll remove you from our mailing list (no hard feelings!). Thank you!

Monday, January 17, 2022

Re: Seminar in Oxford

Dear Mark

Many thanks.

I will be in touch nearer the time with the final arrangements.

Best wishes.

Sara

----- Original Message -----
From: "Mark Gerstein" <mark@gersteinlab.org>
To: "Sara Jolliffe" <Sara.Jolliffe@maths.ox.ac.uk>
Cc: "minary" <Peter.Minary@cs.ox.ac.uk>, "glabstracts mbglab" <glabstracts.mbglab@blogger.com>
Sent: Friday, 14 January, 2022 16:25:16
Subject: Re: Seminar in Oxford

Title:

Data Science Topics Related to Neurogenomics

Abstract:

My seminar will discuss various data-science issues related to
neurogenomics. First, I will focus on classic disorders of the brain,
which affect nearly a fifth of the world's population. Robust
phenotype-genotype associations have been established for several
psychiatric diseases (e.g., schizophrenia, bipolar disorder). However,
understanding their molecular causes is still a challenge. To address
this, the PsychENCODE consortium generated thousands of transcriptome
(bulk and single-cell) datasets from 1,866 individuals. Using these
data, we have developed interpretable machine learning approaches for
deciphering functional genomic elements and linkages in the brain and
psychiatric disorders. Specifically, we developed a deep-learning
model embedding the physical regulatory network to predict phenotype
from genotype. Our model uses a conditional Deep Boltzmann Machine
architecture and introduces lateral connectivity at the visible layer
to embed the biological structure learned from the regulatory network
and QTL linkages. Our model improves disease prediction (6X compared
to additive polygenic risk scores), highlights key genes for
disorders, and imputes missing transcriptome information from genotype
data alone. Next, I will look at the "data exhaust" from this activity
- that is, how one can find other things from the genomic analyses
than what is necessarily intended. I will focus on genomic privacy,
which is a main stumbling block in tackling problems in large-scale
neurogenomics. In particular, I will look at how the quantifications
of expression levels can reveal something about the subjects studied
and how one can take steps to sanitize the data and protect patient
anonymity. Finally, another stumbling block in neurogenomics is more
accurately and precisely phenotyping the individuals. I will discuss
some preliminary work we've done in digital phenotyping.

Friday, January 14, 2022

Re: Seminar in Oxford

Title:

Data Science Topics Related to Neurogenomics

Abstract:

My seminar will discuss various data-science issues related to
neurogenomics. First, I will focus on classic disorders of the brain,
which affect nearly a fifth of the world's population. Robust
phenotype-genotype associations have been established for several
psychiatric diseases (e.g., schizophrenia, bipolar disorder). However,
understanding their molecular causes is still a challenge. To address
this, the PsychENCODE consortium generated thousands of transcriptome
(bulk and single-cell) datasets from 1,866 individuals. Using these
data, we have developed interpretable machine learning approaches for
deciphering functional genomic elements and linkages in the brain and
psychiatric disorders. Specifically, we developed a deep-learning
model embedding the physical regulatory network to predict phenotype
from genotype. Our model uses a conditional Deep Boltzmann Machine
architecture and introduces lateral connectivity at the visible layer
to embed the biological structure learned from the regulatory network
and QTL linkages. Our model improves disease prediction (6X compared
to additive polygenic risk scores), highlights key genes for
disorders, and imputes missing transcriptome information from genotype
data alone. Next, I will look at the "data exhaust" from this activity
- that is, how one can find other things from the genomic analyses
than what is necessarily intended. I will focus on genomic privacy,
which is a main stumbling block in tackling problems in large-scale
neurogenomics. In particular, I will look at how the quantifications
of expression levels can reveal something about the subjects studied
and how one can take steps to sanitize the data and protect patient
anonymity. Finally, another stumbling block in neurogenomics is more
accurately and precisely phenotyping the individuals. I will discuss
some preliminary work we've done in digital phenotyping.