Dear Sir/Ma - I an Ericka, a Financial Consultant in Oman. I work with different private investors that can invest in your projects and ongoing project or a stratup. If you are intrested, i would provide you with more information. Looking forward to speaking with you Best Regards Ericka
Friday, April 10, 2026
Tuesday, March 3, 2026
[ glabstracts.mbglab ] Quota 99.82%
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Tuesday, November 18, 2025
Fwd: DeMets Lecture - request for information
TITLE: AI Methods for Biomedicine: Using Classical, DL & Quantum
Approaches to Address Brain Disorders
ABSTRACT:
This talk surveys a spectrum of AI approaches for biomedicine—from
classical methods to current deep-learning techniques and emerging
extensions involving agents and quantum computing. It focuses on
modeling gene-regulatory and cell-communication networks to interpret
brain disease, describing how cell-type–specific networks can be
constructed from single-cell QTLs and co-expression data and then
embedded into deep-learning frameworks to predict disease from
genotype, prioritize pathways and genes, and model perturbations for
drug-target discovery. The talk then highlights new LLM applications,
including automatic code generation and benchmarking (Biocoder),
collaborative multi-expert clinical reasoning (MedAgents), and
end-to-end single-cell analysis workflows (CellForge). Finally, it
addresses genomic-privacy challenges—first through classical
approaches such as homomorphic encryption and hidden Markov models,
and then through a quantum-computing framework that enables
distributed genomic analysis without exposing individual-level data.
==
i0mad25
Approaches to Address Brain Disorders
ABSTRACT:
This talk surveys a spectrum of AI approaches for biomedicine—from
classical methods to current deep-learning techniques and emerging
extensions involving agents and quantum computing. It focuses on
modeling gene-regulatory and cell-communication networks to interpret
brain disease, describing how cell-type–specific networks can be
constructed from single-cell QTLs and co-expression data and then
embedded into deep-learning frameworks to predict disease from
genotype, prioritize pathways and genes, and model perturbations for
drug-target discovery. The talk then highlights new LLM applications,
including automatic code generation and benchmarking (Biocoder),
collaborative multi-expert clinical reasoning (MedAgents), and
end-to-end single-cell analysis workflows (CellForge). Finally, it
addresses genomic-privacy challenges—first through classical
approaches such as homomorphic encryption and hidden Markov models,
and then through a quantum-computing framework that enables
distributed genomic analysis without exposing individual-level data.
==
i0mad25
Saturday, October 11, 2025
Fwd: *cyan* Invitation to Speak at Mila Bio-AI Seminar Series
**Talk Title**
AI in Biomedicine: Applications in Characterizing Variant Effects and
Drug Binding
**Abstract**
My talk will focus on key applications of deep learning in biomedicine
and artificial intelligence. In particular, I will discuss how deep
learning can be used to understand variant impact and how it has
revolutionized drug discovery and molecular design. Variant discovery
is essential for understanding how human genetic variation contributes
to disease and influences individual responses to pharmaceuticals.
Molecular discovery, in turn, is crucial for developing drugs that
effectively bind to and modulate protein targets. I will cover the
following papers in my talk (+ some others):
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
J Rozowsky et al. (2023). Cell 186: 1493-1511e40.
Leveraging a large language model to predict protein phase transition:
a physical, multiscale and interpretable approach
M Frank et al. (2024). Proc Natl Acad Sci U S A 121: e2320510121.
Predicting Disease-Specific Histone Modifications and Functional
Effects of Non-coding Variants by Leveraging DNA Language Models
X Wang et al. (https://doi.org/10.1101/2025.06.15.659749)
AI in Biomedicine: Applications in Characterizing Variant Effects and
Drug Binding
**Abstract**
My talk will focus on key applications of deep learning in biomedicine
and artificial intelligence. In particular, I will discuss how deep
learning can be used to understand variant impact and how it has
revolutionized drug discovery and molecular design. Variant discovery
is essential for understanding how human genetic variation contributes
to disease and influences individual responses to pharmaceuticals.
Molecular discovery, in turn, is crucial for developing drugs that
effectively bind to and modulate protein targets. I will cover the
following papers in my talk (+ some others):
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
J Rozowsky et al. (2023). Cell 186: 1493-1511e40.
Leveraging a large language model to predict protein phase transition:
a physical, multiscale and interpretable approach
M Frank et al. (2024). Proc Natl Acad Sci U S A 121: e2320510121.
Predicting Disease-Specific Histone Modifications and Functional
Effects of Non-coding Variants by Leveraging DNA Language Models
X Wang et al. (https://doi.org/10.1101/2025.06.15.659749)
Tuesday, October 7, 2025
Fwd: *cyan* Invitation to Speak at Mila Bio-AI Seminar Series
i0mila
**Talk Title**
AI in Biomedicine: Applications in Characterizing Variant Effects and
Drug Binding
**Abstract**
I will provide a more detailed abstract shortly, but for now, I plan
to cover material related to the following papers (+ some others):
The EN-TEx resource of multi-tissue personal epigenomes &
variant-impact models**
J Rozowsky et al. (2023). Cell 186: 1493-1511e40.
Leveraging a large language model to predict protein phase transition:
a physical, multiscale and interpretable approach**
M Frank et al. (2024). Proc Natl Acad Sci U S A 121: e2320510121.
Predicting Disease-Specific Histone Modifications and Functional
Effects of Non-coding Variants by Leveraging DNA Language Models**
X Wang et al.
(https://doi.org/10.1101/2025.06.15.659749)
**Talk Title**
AI in Biomedicine: Applications in Characterizing Variant Effects and
Drug Binding
**Abstract**
I will provide a more detailed abstract shortly, but for now, I plan
to cover material related to the following papers (+ some others):
The EN-TEx resource of multi-tissue personal epigenomes &
variant-impact models**
J Rozowsky et al. (2023). Cell 186: 1493-1511e40.
Leveraging a large language model to predict protein phase transition:
a physical, multiscale and interpretable approach**
M Frank et al. (2024). Proc Natl Acad Sci U S A 121: e2320510121.
Predicting Disease-Specific Histone Modifications and Functional
Effects of Non-coding Variants by Leveraging DNA Language Models**
X Wang et al.
(https://doi.org/10.1101/2025.06.15.659749)
Friday, July 25, 2025
Fwd: visiting Cambridge/London in July
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
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
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
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