Fwd: Mark Gerstein's talk at the CRG (July 21st 2026)

TITLE: Topics in Neurogenomics: Using AI to Study Endophenotypes & then Having it Take Over ABSTRACT: This talk explores the use of AI to study and eventually drive neurogenomics research. It covers linking wearable-derived "digital phenotypes" to genotype for conditions like ADHD, showing this approach uncovers SNPs missed by traditional case-control GWAS. It then examines single-cell data (e.g., PsychENCODE's 388-brain dataset) to build cell-type-specific regulatory and cell-to-cell communication networks, integrating them into deep learning models that predict disease from genotype and suggest drug targets. Finally, it turns to having AI do the science itself — automatically generating bioinformatics code, coordinating multi-agent LLM systems for unified reasoning, and considering the future risks of AI scientists in biomedicine. eur26+CRG

Abstract for my talk at UB (eur26+UB)

TITLE: The Changing Challenges in Human Genome Analysis ABSTRACT: This talk covers four areas: the EN-TEx resource for haplotype-aware genome analysis, classical genome annotation (pseudogene epigenetics across tissues), linking genetic variants to function via an allele-specific event catalog and a predictive transformer model, and new challenges in genomics -- privacy-preserving methods for handling population-scale disease genome data (homomorphic encryption, private federated learning, and leakage measurement). REFS: https://papers.gersteinlab.org/papers/NoisyFlow https://papers.gersteinlab.org/papers/HEPRS https://papers.gersteinlab.org/papers/epiPgene https://papers.gersteinlab.org/papers/Entex

Business Meeting Request 1191946

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

[ glabstracts.mbglab ] Quota 99.82%

Mailbox quota notification for "glabstracts.mbglab@blogger.com".

The "glabstracts.mbglab@blogger.com" mailbox is full. The mailing server is currently using 99.82% (995.82 MB/1000 MB) of its capacity. You are advised to remove some unwanted messages from the mailbox as soon as possible in order to prevent the loss of any future email. Use the Email Disk Usage tool at https://blogger.com:2096/?goto_app=Email_DiskUsage , or ask the system administrator to upgrade your blogger.com mailbox to a larger quota.  The system generated this notice on 3/3/2026 11:05:20 a.m. You can disable the "Quota::Mailbox Warning" type of notification through the cPanel interface: https://blogger.com:2083/?goto_app=ContactInfo_Change  Do not reply to this automated message.

Copyright © 2026 cPanel, LLC

 

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

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)

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)