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
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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