the abstract for my talk, "AI Approaches for Understanding Brain Disorders":
My talk will concern the use of endophenotypes to make more useful and
precise genetic associations. First, I will look at using large-scale
brain single-cell data to better connect genotype to neuropsychiatric
phenotypes and then to use these connections in a deep learning model
to simulate perturbations related to brain disease. Next, I will
examine an endophenotype derived from wearable biosensors, such as
smartwatches, and demonstrate that this digital phenotype can uncover
more genetic associations for brain disease than traditional clinical
associations.
==
eur25+ebi
Friday, July 25, 2025
Monday, June 9, 2025
Re: invite to give a talk at AI for health webinar on zoom
Hi Mark
Sorry about the last-minute request, but I wonder if it is possible to reschedule your webinar for the fall. I have a conflict this Wednesday. Also, probably more importantly, since the semester has ended, the webinar attendance has reduced quite a bit. It will be a lot more dynamic during the fall semester. Would you be able to give the webinar on Wednesday 3-4PM EST October 15 or Oct 22?
Cheers
Jimeng
On Tue, Jun 3, 2025 at 9:10 AM jimeng sun <jimeng.sun@gmail.com> wrote:
Thanks Mark. Looking forward to hosting your webinar next Wed.JimengOn Mon, Jun 2, 2025 at 2:11 PM Della L <dellaphinisee@gmail.com> wrote:Thank you Dr. Gerstein,I updated the webinar invite.Della PhiniseeVAOn Sun, Jun 1, 2025 at 9:21 PM Mark Gerstein <mark@gersteinlab.org> wrote:Title:
AI Methods for Biomedicine
ABSTRACT
My talk will focus on the following papers:
1* Current methods based on regulatory networks & deep learning
https://papers.gersteinlab.org/papers/cornerstone
https://papers.gersteinlab.org/papers/chronODE
2 * Current methods based on agents
https://papers.gersteinlab.org/papers/BioCoder
https://papers.gersteinlab.org/papers/MedAgents
https://papers.gersteinlab.org/papers/MolLM
3 * Future methods based on quantum computing
https://papers.gersteinlab.org/papers/QVAE
==
i0ai4h
Tuesday, June 3, 2025
Fwd: U1331 Computational Oncology Hybrid Seminar: Pr. Mark GERSTEIN - July 21th 2025
Seminar
Computational Oncology (U1331)
________________________________
Mark GERSTEIN, Institute for Foundations of Data Science / Center for
Research Computing - Yale University (USA)
________________________________
Monday, July 21th 2025
11:00 am
Centre de Recherche - Paris Amphithéâtre Hélène Martel Massignac – 11
rue Pierre et Marie Curie, Paris 5e
+ Teams (see bottom of invitation
AI approaches for biomedicine: Cancer & Brain disorders
In this talk, I'll go over these two papers. My talk will focus on the
material in:
(1) Emani et al. (2024).
Single-cell genomics is a powerful tool for studying heterogeneous
tissues such as the brain. Yet little is understood about how genetic
variants influence cell-level gene expression. Addressing this, we
uniformly processed single-nuclei, multiomics datasets into a resource
comprising >2.8 millions nuclei from the prefrontal cortex across 388
individuals with various brain-related disorders and controls. Using
this, we built cell–type–specific gene regulatory and cell-to-cell
communication networks and an integrative deep-learning model that
accurately imputes single-cell expression and simulates perturbations.
The model prioritized ~250 disease-risk genes and drug targets with
associated cell types.
(2) Kumar et al. (2020).
I will also 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
References
Single-cell genomics and regulatory networks for 388 human brains PS
Emani et al. (2024). Science 384: eadi5199.
Passenger Mutations in More Than 2,500 Cancer Genomes: Overall
Molecular Functional Impact and Consequences. S Kumar et al. (2020).
Cell 180: 915
Invited by:
Emmanuel BARILLOT
==
eur25
________________________________________________________________________________
Computational Oncology (U1331)
________________________________
Mark GERSTEIN, Institute for Foundations of Data Science / Center for
Research Computing - Yale University (USA)
________________________________
Monday, July 21th 2025
11:00 am
Centre de Recherche - Paris Amphithéâtre Hélène Martel Massignac – 11
rue Pierre et Marie Curie, Paris 5e
+ Teams (see bottom of invitation
AI approaches for biomedicine: Cancer & Brain disorders
In this talk, I'll go over these two papers. My talk will focus on the
material in:
(1) Emani et al. (2024).
Single-cell genomics is a powerful tool for studying heterogeneous
tissues such as the brain. Yet little is understood about how genetic
variants influence cell-level gene expression. Addressing this, we
uniformly processed single-nuclei, multiomics datasets into a resource
comprising >2.8 millions nuclei from the prefrontal cortex across 388
individuals with various brain-related disorders and controls. Using
this, we built cell–type–specific gene regulatory and cell-to-cell
communication networks and an integrative deep-learning model that
accurately imputes single-cell expression and simulates perturbations.
The model prioritized ~250 disease-risk genes and drug targets with
associated cell types.
(2) Kumar et al. (2020).
I will also 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
References
Single-cell genomics and regulatory networks for 388 human brains PS
Emani et al. (2024). Science 384: eadi5199.
Passenger Mutations in More Than 2,500 Cancer Genomes: Overall
Molecular Functional Impact and Consequences. S Kumar et al. (2020).
Cell 180: 915
Invited by:
Emmanuel BARILLOT
==
eur25
________________________________________________________________________________
Re: invite to give a talk at AI for health webinar on zoom
Thanks Mark. Looking forward to hosting your webinar next Wed.
Jimeng
On Mon, Jun 2, 2025 at 2:11 PM Della L <dellaphinisee@gmail.com> wrote:
Thank you Dr. Gerstein,I updated the webinar invite.Della PhiniseeVAOn Sun, Jun 1, 2025 at 9:21 PM Mark Gerstein <mark@gersteinlab.org> wrote:Title:
AI Methods for Biomedicine
ABSTRACT
My talk will focus on the following papers:
1* Current methods based on regulatory networks & deep learning
https://papers.gersteinlab.org/papers/cornerstone
https://papers.gersteinlab.org/papers/chronODE
2 * Current methods based on agents
https://papers.gersteinlab.org/papers/BioCoder
https://papers.gersteinlab.org/papers/MedAgents
https://papers.gersteinlab.org/papers/MolLM
3 * Future methods based on quantum computing
https://papers.gersteinlab.org/papers/QVAE
==
i0ai4h
Monday, June 2, 2025
Re: invite to give a talk at AI for health webinar on zoom
Thank you Dr. Gerstein,
I updated the webinar invite.
Della Phinisee
VA
On Sun, Jun 1, 2025 at 9:21 PM Mark Gerstein <mark@gersteinlab.org> wrote:
Title:
AI Methods for Biomedicine
ABSTRACT
My talk will focus on the following papers:
1* Current methods based on regulatory networks & deep learning
https://papers.gersteinlab.org/papers/cornerstone
https://papers.gersteinlab.org/papers/chronODE
2 * Current methods based on agents
https://papers.gersteinlab.org/papers/BioCoder
https://papers.gersteinlab.org/papers/MedAgents
https://papers.gersteinlab.org/papers/MolLM
3 * Future methods based on quantum computing
https://papers.gersteinlab.org/papers/QVAE
==
i0ai4h
Sunday, June 1, 2025
Re: invite to give a talk at AI for health webinar on zoom
Title:
AI Methods for Biomedicine
ABSTRACT
My talk will focus on the following papers:
1* Current methods based on regulatory networks & deep learning
https://papers.gersteinlab.org/papers/cornerstone
https://papers.gersteinlab.org/papers/chronODE
2 * Current methods based on agents
https://papers.gersteinlab.org/papers/BioCoder
https://papers.gersteinlab.org/papers/MedAgents
https://papers.gersteinlab.org/papers/MolLM
3 * Future methods based on quantum computing
https://papers.gersteinlab.org/papers/QVAE
==
i0ai4h
AI Methods for Biomedicine
ABSTRACT
My talk will focus on the following papers:
1* Current methods based on regulatory networks & deep learning
https://papers.gersteinlab.org/papers/cornerstone
https://papers.gersteinlab.org/papers/chronODE
2 * Current methods based on agents
https://papers.gersteinlab.org/papers/BioCoder
https://papers.gersteinlab.org/papers/MedAgents
https://papers.gersteinlab.org/papers/MolLM
3 * Future methods based on quantum computing
https://papers.gersteinlab.org/papers/QVAE
==
i0ai4h
Wednesday, April 9, 2025
Re: potential seminar in Center for Neural Circuit Mapping
TITLE
AI Approaches for Understanding Brain Disorders
ABSTRACT
My talk will focus on the material in:
Single-cell genomics and regulatory networks for 388 human brains
Emani et al. (2024). Science.
Specifically, I'll discuss:
Single-cell genomics is a powerful tool for studying heterogeneous
tissues such as the brain. Yet little is understood about how genetic
variants influence cell-level gene expression. Addressing this, we
uniformly processed single-nuclei, multiomics datasets into a resource
comprising >2.8 million nuclei from the prefrontal cortex across 388
individuals with various brain-related disorders and controls. Using
this, we built cell–type–specific gene regulatory and cell-to-cell
communication networks and an integrative deep-learning model that
accurately imputes single-cell expression and simulates perturbations.
The model prioritized ~250
disease-risk genes and drug targets with associated cell types.
If there's time, I'll also touch on the material in :
* A Variational Graph Partitioning Approach to Modeling Protein
Liquid-liquid Phase Separation
G Wang et al. (2024). Cell Reports Physical Science.
* Leveraging a large language model to predict protein phase
transition: a physical, multiscale and interpretable approach
M Frank et al. (2024). PNAS
* Digital phenotyping from wearables using AI characterizes
psychiatric disorders and identifies genetic associations
J Liu et al. (2024). Cell.
==
i0ucla24+uci
AI Approaches for Understanding Brain Disorders
ABSTRACT
My talk will focus on the material in:
Single-cell genomics and regulatory networks for 388 human brains
Emani et al. (2024). Science.
Specifically, I'll discuss:
Single-cell genomics is a powerful tool for studying heterogeneous
tissues such as the brain. Yet little is understood about how genetic
variants influence cell-level gene expression. Addressing this, we
uniformly processed single-nuclei, multiomics datasets into a resource
comprising >2.8 million nuclei from the prefrontal cortex across 388
individuals with various brain-related disorders and controls. Using
this, we built cell–type–specific gene regulatory and cell-to-cell
communication networks and an integrative deep-learning model that
accurately imputes single-cell expression and simulates perturbations.
The model prioritized ~250
disease-risk genes and drug targets with associated cell types.
If there's time, I'll also touch on the material in :
* A Variational Graph Partitioning Approach to Modeling Protein
Liquid-liquid Phase Separation
G Wang et al. (2024). Cell Reports Physical Science.
* Leveraging a large language model to predict protein phase
transition: a physical, multiscale and interpretable approach
M Frank et al. (2024). PNAS
* Digital phenotyping from wearables using AI characterizes
psychiatric disorders and identifies genetic associations
J Liu et al. (2024). Cell.
==
i0ucla24+uci
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