Re: ISMB/ECCB 2023 Keynote Information

Added, thanks!

Seth Munholland, Ph.D., DES

Lead Technologist

International Society for Computational Biology

525-K East Market Street, RM 330

Leesburg, VA, USA 20176

T: (403) 831-2587

On Tue, Jul 4, 2023 at 10:46 AM Steven Leard <steven@marketwhys.ca> wrote:

thanks Mark - Seth will get this added shorty, 

thanks,

Steven

Steven Leard

ISMB Conference Director

steven@marketwhys.ca

On Jul 4, 2023, at 10:45 AM, Mark Gerstein <mark@gersteinlab.org> wrote:

Title:
A Gradual Evolution in Bioinformatics Research

Abstract:
This talk will survey my lab's computational biology research from the
1990s to the present and touch upon the broader directions of the
field. I'll start by discussing some early work on structure and
simulation and how this transitioned into studying protein and gene
networks and, eventually, human genome annotation with
machine-learning models. Finally, I'll discuss some of our current
work developing models for variant impact based on allele-specific
binding and how this highlights the role of transcription-factor
binding motifs that are particularly sensitive to mutation. I'll end
with thoughts on where I think the field is going. In particular, I'll
discuss the importance of dealing with large amounts of private data
and using digital sensors to phenotype individuals more accurately.

==
i0ismb23

Re: ISMB/ECCB 2023 Keynote Information

thanks Mark - Seth will get this added shorty, 

thanks,

Steven

Steven Leard

ISMB Conference Director

steven@marketwhys.ca

On Jul 4, 2023, at 10:45 AM, Mark Gerstein <mark@gersteinlab.org> wrote:

Title:
A Gradual Evolution in Bioinformatics Research

Abstract:
This talk will survey my lab's computational biology research from the
1990s to the present and touch upon the broader directions of the
field. I'll start by discussing some early work on structure and
simulation and how this transitioned into studying protein and gene
networks and, eventually, human genome annotation with
machine-learning models. Finally, I'll discuss some of our current
work developing models for variant impact based on allele-specific
binding and how this highlights the role of transcription-factor
binding motifs that are particularly sensitive to mutation. I'll end
with thoughts on where I think the field is going. In particular, I'll
discuss the importance of dealing with large amounts of private data
and using digital sensors to phenotype individuals more accurately.

==
i0ismb23

Fwd: ISMB/ECCB 2023 Keynote Information

Title:
A Gradual Evolution in Bioinformatics Research

Abstract:
This talk will survey my lab's computational biology research from the
1990s to the present and touch upon the broader directions of the
field. I'll start by discussing some early work on structure and
simulation and how this transitioned into studying protein and gene
networks and, eventually, human genome annotation with
machine-learning models. Finally, I'll discuss some of our current
work developing models for variant impact based on allele-specific
binding and how this highlights the role of transcription-factor
binding motifs that are particularly sensitive to mutation. I'll end
with thoughts on where I think the field is going. In particular, I'll
discuss the importance of dealing with large amounts of private data
and using digital sensors to phenotype individuals more accurately.

==
i0ismb23

Re: The Festival of Genomics and Biodata, October 2023, Boston

Title & Bullets for Festival of Genomics Talk

Personal Functional Genomics

* EN-TEx includes >1500 datasets (>25 tissues X 10 assays) mapped to 4
personal genomes
* Comprehensive catalog of allele-specific activity, decorating
regulatory elements
* Model for transferring known eQTLs to difficult-to-profile tissues
(eg skin=>heart)
* Transformer model for predicting allelic activity based on local
sequence context

i0flg23

Invited Session for JSM 2023

Title: Single Cell Data Analysis: Computational methods for
characterizing cell types

I will describe two techniques for the analysis of single-cell
sequencing data. (1) Forest Fire Clustering. This is an efficient and
interpretable method for cell-type discovery from single-cell data. It
makes minimal prior assumptions and, different from current
approaches, calculates a non-parametric posterior probability that
each cell is assigned a cell-type label. These posterior distributions
allow for the evaluation of a label confidence for each cell and
enable the computation of "label entropies," highlighting transitions
along developmental trajectories. (2) SCAN-ATAC-Sim. It is difficult
to benchmark the performance of various scATAC-seq analysis techniques
(such as clustering and deconvolution) without having a priori a known
set of gold-standard cell types. To simulate scATAC-seq experiments
with known cell-type labels, we introduce an efficient and scalable
scATAC-seq simulation method that down-samples bulk ATAC-seq data
(e.g., from representative cell lines or tissues). Our protocol uses a
consistent but tunable signal-to-noise ratio across cell types in a
scATAC-seq simulation.

SCAN-ATAC-Sim: a scalable and efficient method for simulating
single-cell ATAC-seq data from bulk-tissue experiments.
Z Chen, J Zhang, J Liu, Z Zhang, J Zhu, D Lee, M Xu, M Gerstein
(2021). Bioinformatics 37: 1756-8.

Forest Fire Clustering for single-cell sequencing combines iterative
label propagation with parallelized Monte Carlo simulations.
Z Chen, J Goldwasser, P Tuckman, J Liu, J Zhang, M Gerstein (2022).
Nat Commun 13: 3538.