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
Sunday, December 22, 2024
Re: [EXTERNAL] Re: Invitation to Science Forum Microsoft Research AI for Science
Title:
AI Approaches for Understanding Brain Disorders:
Schizophrenia, Alzheimer's disease & ADHD
Abstract:
My talk will focus on the material in:
Emani et al. (2024). Science.
Single-cell genomics and regulatory networks for 388 human brains
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. For 28
harmonized neuronal and non-neuronal cell types, we assessed
population-level variation in expression and chromatin across gene
families and drug targets. Integration of expression and genotype data
revealed >1.4 million single-cell expression quantitative trait loci
(eQTLs), many of which were not seen in bulk gene-expression datasets.
The chromatin datasets allowed for the identification of >550,000
single-cell cis-regulatory elements enriched at loci linked to
brain-related traits. Combining expression, chromatin, and eQTL
datasets, we built cell type–specific gene regulatory and cell-to-cell
communication networks, which manifest cellular changes in aging and
neuropsychiatric disorders, including altered Wnt signaling in
schizophrenia and bipolar disorder. We further constructed 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,
suggesting potential precision-medicine approaches for
neuropsychiatric disorders.
If there's time, I'll also touch on the material in :
* A Variational Graph Partitioning Approach to Modeling Protein
Liquid-liquid Phase Separation
Gaoyuan Wang, Jonathan H Warrell, Suchen Zheng, Mark Gerstein (2023).
Cell Reports Physical Science.
* Leveraging a large language model to predict protein phase
transition: a physical, multiscale and interpretable approach
M Frank, P Ni, M Jensen, MB Gerstein (2024). Proc Natl Acad Sci U S A
121: e2320510121.
* Digital phenotyping from wearables using AI characterizes
psychiatric disorders and identifies genetic associations
JJ Liu, B Borsari, Y Li, SX Liu, Y Gao, X Xin, S Lou, M Jensen, D
Garrido-Martin, TL Verplaetse, G Ash, J Zhang, MJ Girgenti, W Roberts,
M Gerstein (2024). Cell.
i0msft
AI Approaches for Understanding Brain Disorders:
Schizophrenia, Alzheimer's disease & ADHD
Abstract:
My talk will focus on the material in:
Emani et al. (2024). Science.
Single-cell genomics and regulatory networks for 388 human brains
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. For 28
harmonized neuronal and non-neuronal cell types, we assessed
population-level variation in expression and chromatin across gene
families and drug targets. Integration of expression and genotype data
revealed >1.4 million single-cell expression quantitative trait loci
(eQTLs), many of which were not seen in bulk gene-expression datasets.
The chromatin datasets allowed for the identification of >550,000
single-cell cis-regulatory elements enriched at loci linked to
brain-related traits. Combining expression, chromatin, and eQTL
datasets, we built cell type–specific gene regulatory and cell-to-cell
communication networks, which manifest cellular changes in aging and
neuropsychiatric disorders, including altered Wnt signaling in
schizophrenia and bipolar disorder. We further constructed 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,
suggesting potential precision-medicine approaches for
neuropsychiatric disorders.
If there's time, I'll also touch on the material in :
* A Variational Graph Partitioning Approach to Modeling Protein
Liquid-liquid Phase Separation
Gaoyuan Wang, Jonathan H Warrell, Suchen Zheng, Mark Gerstein (2023).
Cell Reports Physical Science.
* Leveraging a large language model to predict protein phase
transition: a physical, multiscale and interpretable approach
M Frank, P Ni, M Jensen, MB Gerstein (2024). Proc Natl Acad Sci U S A
121: e2320510121.
* Digital phenotyping from wearables using AI characterizes
psychiatric disorders and identifies genetic associations
JJ Liu, B Borsari, Y Li, SX Liu, Y Gao, X Xin, S Lou, M Jensen, D
Garrido-Martin, TL Verplaetse, G Ash, J Zhang, MJ Girgenti, W Roberts,
M Gerstein (2024). Cell.
i0msft
Tuesday, November 26, 2024
Re: [EXTERNAL] Re: Invitation to speak at the MD Anderson Virtual Data Science Forum
Hi Mark,
Fine. We'll add that in the morning to the already-posted calendar version (which included the title) and to the ListServ.
Again, best wishes. Bissan will be introducing you, so she can ask for any additional info if needed.
Best,
John and Bissan
The information contained in this e-mail message may be privileged, confidential, and/or protected from disclosure. This e-mail message may contain protected health information (PHI); dissemination of PHI should comply with applicable federal and state laws. If you are not the intended recipient, or an authorized representative of the intended recipient, any further review, disclosure, use, dissemination, distribution, or copying of this message or any attachment (or the information contained therein) is strictly prohibited. If you think that you have received this e-mail message in error, please notify the sender by return e-mail and delete all references to it and its contents from your systems.
Re: [EXTERNAL] Re: Invitation to speak at the MD Anderson Virtual Data Science Forum
TITLE:
Topics in Cancer Genomics
ABSTRACT:
My talk will cover material from the following papers:
Using sigLASSO to optimize cancer mutation signatures jointly with
sampling likelihood.
S Li, FW Crawford, MB Gerstein (2020). Nat Commun 11: 3575.
Latent Evolutionary Signatures: A General Framework for Analyzing
Music and Cultural Evolution
J Warrell, L Salichos, M Gancz, MB Gerstein (2024). J R Soc Interface
21: 20230647.
Assessing and mitigating privacy risks of sparse, noisy genotypes by
local alignment to haplotype databases.
PS Emani, MN Geradi, G Gursoy, MR Grasty, A Miranker, MB Gerstein
(2023). Genome Res 33: 2156-2173.
Unified views on variant impact across many diseases
S Kumar, M Gerstein (2023). Trends Genet 39: 442-450.
Passenger Mutations in More Than 2,500 Cancer Genomes: Overall
Molecular Functional Impact and Consequences.
S Kumar, J Warrell, S Li, PD McGillivray, W Meyerson, L Salichos, A
Harmanci, A Martinez-Fundichely, CWY Chan, MM Nielsen, L Lochovsky, Y
Zhang, X Li, S Lou, JS Pedersen, C Herrmann, G Getz, E Khurana, MB
Gerstein (2020). Cell 180: 915-927e16.
Origins and characterization of variants shared between databases of
somatic and germline human mutations.
W Meyerson, J Leisman, FCP Navarro, M Gerstein (2020). BMC
Bioinformatics 21: 227.
i0md24
Topics in Cancer Genomics
ABSTRACT:
My talk will cover material from the following papers:
Using sigLASSO to optimize cancer mutation signatures jointly with
sampling likelihood.
S Li, FW Crawford, MB Gerstein (2020). Nat Commun 11: 3575.
Latent Evolutionary Signatures: A General Framework for Analyzing
Music and Cultural Evolution
J Warrell, L Salichos, M Gancz, MB Gerstein (2024). J R Soc Interface
21: 20230647.
Assessing and mitigating privacy risks of sparse, noisy genotypes by
local alignment to haplotype databases.
PS Emani, MN Geradi, G Gursoy, MR Grasty, A Miranker, MB Gerstein
(2023). Genome Res 33: 2156-2173.
Unified views on variant impact across many diseases
S Kumar, M Gerstein (2023). Trends Genet 39: 442-450.
Passenger Mutations in More Than 2,500 Cancer Genomes: Overall
Molecular Functional Impact and Consequences.
S Kumar, J Warrell, S Li, PD McGillivray, W Meyerson, L Salichos, A
Harmanci, A Martinez-Fundichely, CWY Chan, MM Nielsen, L Lochovsky, Y
Zhang, X Li, S Lou, JS Pedersen, C Herrmann, G Getz, E Khurana, MB
Gerstein (2020). Cell 180: 915-927e16.
Origins and characterization of variants shared between databases of
somatic and germline human mutations.
W Meyerson, J Leisman, FCP Navarro, M Gerstein (2020). BMC
Bioinformatics 21: 227.
i0md24
Thursday, November 14, 2024
Hi Dear
Hi Dear,
I trust this message finds you well.
Permit me to introduce myself. I'm John Romano. I'm looking for woman who's as interested in a long-term relationship as I am.
Three words to describe me: adventurous, optimistic, spontaneous. I am a medical sales personnel, so I'm always open to new possibilities. Seeking casual dates and lots of fun.
Am sorry if i invaded your private space. Apologies. If you want to find out more about me feel free to send me a message and I will be glad to share more about myself with you and send you some of my recent pictures.
Enjoy your day and I look forward to hearing from you!
Cheers,
John
I trust this message finds you well.
Permit me to introduce myself. I'm John Romano. I'm looking for woman who's as interested in a long-term relationship as I am.
Three words to describe me: adventurous, optimistic, spontaneous. I am a medical sales personnel, so I'm always open to new possibilities. Seeking casual dates and lots of fun.
Am sorry if i invaded your private space. Apologies. If you want to find out more about me feel free to send me a message and I will be glad to share more about myself with you and send you some of my recent pictures.
Enjoy your day and I look forward to hearing from you!
Cheers,
John
Sunday, June 16, 2024
Re: Abstract Required | From Genetic Discoveries to Gene Function in Human Diseases Conference, 13 - 16 July 2024, Portugal
Title: Single-cell genomics and regulatory networks for 388 human brains
Speaker: Mark Gerstein
Abstract:
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. For 28
harmonized neuronal and non-neuronal cell types, we assessed
population-level variation in expression and chromatin across gene
families and drug targets. Integration of expression and genotype data
revealed >1.4 million single-cell expression quantitative trait loci
(eQTLs), many of which were not seen in bulk gene-expression datasets.
The chromatin datasets allowed for the identification of >550,000
single-cell cis-regulatory elements enriched at loci linked to
brain-related traits. Combining expression, chromatin, and eQTL
datasets, we built cell type–specific gene regulatory and cell-to-cell
communication networks, which manifest cellular changes in aging and
neuropsychiatric disorders, including altered Wnt signaling in
schizophrenia and bipolar disorder. We further constructed 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,
suggesting potential precision-medicine approaches for
neuropsychiatric disorders.
==
i0lis24, eur24
On Wed, Jun 12, 2024 at 5:41 AM Rosie Johnson
<Rosie@fusion-conferences.com> wrote:
>
> Dear Speakers,
>
>
>
> Please submit your abstract for the 'From Genetic Discoveries to Gene Function in Human Diseases' conference by Friday 21st June 2024 for inclusion in the conference abstract book.
>
>
>
> We are looking forward to welcoming you to Portugal very soon!
>
>
>
> Thank you in advance for your cooperation.
>
>
>
> Best wishes,
>
>
>
> Rosie Johnson | Conference Manager
>
>
>
> T: +44 (0) 1638 555057
>
> DD: +44 (0) 1638 599351
>
> E: rosie@fusion-conferences.com
>
> Twitter: @Fusion_Conf
>
>
>
> Please note the Fusion office is open Mon - Thurs (08.30 – 17:00).
>
>
>
> *SCAM EMAILS* Please be vigilant on email and phone, we have been made aware that our speakers are being approached by several scam companies (Travel Hosting, Traveller Point, E Hotel Services or similar) regarding accommodation for Fusion meetings. Communications relating to our meetings will always come from a named Fusion team member and our email domain will always end in '@fusion-conferences.com'. For peace of mind, you can visit our website at any time to familiarise yourself with the Fusion team.
>
>
>
> Fusion Conferences Ltd, Unit 5D Lynx Business Park, Fordham Road, Snailwell, Cambridgeshire, CB8 7NY. Email and the internet are not considered secure media. Please note that we cannot accept liability for any data intercepted and processed. We process personal data contained herein in accordance with UK data protection legislation. All feasible security measures are in place. For further information, please see our Privacy Policy. If you are not the intended recipient of this email, please let the sender know and then destroy all copies. Thank you.
>
>
Speaker: Mark Gerstein
Abstract:
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. For 28
harmonized neuronal and non-neuronal cell types, we assessed
population-level variation in expression and chromatin across gene
families and drug targets. Integration of expression and genotype data
revealed >1.4 million single-cell expression quantitative trait loci
(eQTLs), many of which were not seen in bulk gene-expression datasets.
The chromatin datasets allowed for the identification of >550,000
single-cell cis-regulatory elements enriched at loci linked to
brain-related traits. Combining expression, chromatin, and eQTL
datasets, we built cell type–specific gene regulatory and cell-to-cell
communication networks, which manifest cellular changes in aging and
neuropsychiatric disorders, including altered Wnt signaling in
schizophrenia and bipolar disorder. We further constructed 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,
suggesting potential precision-medicine approaches for
neuropsychiatric disorders.
==
i0lis24, eur24
On Wed, Jun 12, 2024 at 5:41 AM Rosie Johnson
<Rosie@fusion-conferences.com> wrote:
>
> Dear Speakers,
>
>
>
> Please submit your abstract for the 'From Genetic Discoveries to Gene Function in Human Diseases' conference by Friday 21st June 2024 for inclusion in the conference abstract book.
>
>
>
> We are looking forward to welcoming you to Portugal very soon!
>
>
>
> Thank you in advance for your cooperation.
>
>
>
> Best wishes,
>
>
>
> Rosie Johnson | Conference Manager
>
>
>
> T: +44 (0) 1638 555057
>
> DD: +44 (0) 1638 599351
>
> E: rosie@fusion-conferences.com
>
> Twitter: @Fusion_Conf
>
>
>
> Please note the Fusion office is open Mon - Thurs (08.30 – 17:00).
>
>
>
> *SCAM EMAILS* Please be vigilant on email and phone, we have been made aware that our speakers are being approached by several scam companies (Travel Hosting, Traveller Point, E Hotel Services or similar) regarding accommodation for Fusion meetings. Communications relating to our meetings will always come from a named Fusion team member and our email domain will always end in '@fusion-conferences.com'. For peace of mind, you can visit our website at any time to familiarise yourself with the Fusion team.
>
>
>
> Fusion Conferences Ltd, Unit 5D Lynx Business Park, Fordham Road, Snailwell, Cambridgeshire, CB8 7NY. Email and the internet are not considered secure media. Please note that we cannot accept liability for any data intercepted and processed. We process personal data contained herein in accordance with UK data protection legislation. All feasible security measures are in place. For further information, please see our Privacy Policy. If you are not the intended recipient of this email, please let the sender know and then destroy all copies. Thank you.
>
>
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