BEGIN:VCALENDAR
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PRODID:researchseminars.org
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BEGIN:VEVENT
SUMMARY:Guocheng Yuan
DTSTART:20200615T113000Z
DTEND:20200615T123000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/1
DESCRIPTION:Title: Keynote Talk (Cortex seq-FISH study)\nby Guocheng Yuan as
part of Mathematical Frameworks for Integrative Analysis of Emerging Biolo
gical Data Types\n\n\nAbstract\nDr Guocheng Yuan (Dana-Farber Cancer Insti
tute\, Harvard TH Chan School of Public Health) Lab Website: http://bcb.df
ci.harvard.edu/~gcyuan GC will present the SeqFish hackathon study\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexis Coullomb
DTSTART:20200615T130000Z
DTEND:20200615T132000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/3
DESCRIPTION:Title: CORTEX seq-FISH: clustering\nby Alexis Coullomb as part of
Mathematical Frameworks for Integrative Analysis of Emerging Biological D
ata Types\n\n\nAbstract\nAlexis Coullomb is a member of Vera Pancaldi's La
b at Cancer Research Centre of Toulouse\, INSERM\, France\, https://www.cr
ct-inserm.fr/personne/alexis-coullomb/ Alexis Coullomb will present analys
is in which addressed the questions: * Can scRNA-seq data be overlaid onto
seqFISH for resolution enhancement? * What is the minimal number of genes
needed for data integration? Alexis Coullomb was also interested in how c
ould we detect specific spatial areas given the seqFISH gene expression da
ta and by reconstruction the spatial network of cells. Code is available a
t: https://github.com/AlexCoul/multiOmics_integration\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hang Xu
DTSTART:20200615T132000Z
DTEND:20200615T134000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/4
DESCRIPTION:Title: CORTEX seq-FISH: selection of spatial coherent genes\nby H
ang Xu as part of Mathematical Frameworks for Integrative Analysis of Emer
ging Biological Data Types\n\n\nAbstract\nDr Hang Xu is a postdoctoral Fel
low in Christina Curtis's Lab (Stanford). Hang obtained her PhD in bioinfo
rmatics at the University of Nottingham\, UK. She then trained as a postdo
ctoral research fellow in the Francis Crick Institute with Charles Swanton
. Hang is interested in studying cancer evolutionary dynamics. Her researc
h asked the following questions\; 1. Can scRNA-seq data be overlaid onto s
eqFISH for resolution enhancement? 2. What is the minimal number of genes
needed for data integration? She followed the approaches that described in
Zhu's paper (Zhu et al 2018) which integrated scRNAseq and smFISH data. B
y following the approach\, she randomly selected a subset of differently e
xpressed genes and applied a SVM model to estimated the minimal number of
genes that are required data integration. Code is available at https://git
hub.com/gooday23/smfishscRNAHackathon/\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dario Righelli
DTSTART:20200615T134000Z
DTEND:20200615T140000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/5
DESCRIPTION:Title: CORTEX seq-FISH: software structure and data integration\n
by Dario Righelli as part of Mathematical Frameworks for Integrative Analy
sis of Emerging Biological Data Types\n\n\nAbstract\nDr. Dario Righelli is
a postdoctoral fellow in the Department of Statistics\, University of Pad
ua\, Italy (https://www.researchgate.net/profile/Dario_Righelli) in the la
b of Dr. Davide Risso His work focused on the software infrastructure need
ed to easily analyze spatial datasets (such as seqFISH\, 10X Visium\, etc.
) and to integrate spatial and non-spatial datasets Code is available at h
ttps://github.com/drighelli/SpatialAnalysis\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joshua Sodicoff
DTSTART:20200615T144000Z
DTEND:20200615T150000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/6
DESCRIPTION:Title: CORTEX seq-FISH: integration with scRNA-seq data\nby Joshu
a Sodicoff as part of Mathematical Frameworks for Integrative Analysis of
Emerging Biological Data Types\n\n\nAbstract\nMr. Joshua Sodicoff is a Dat
a Sciences BS Student in the lab of Joshua Welsh at the University of Mich
igan Medical School (https://welch-lab.github.io/people/ ). He addressed t
he first two questions listed on the github page for the seqfish data. Our
primary goal was to integrate seqFISH data with scRNA-seq data to increas
e resolution and utilize LIGER to account for dataset-specific differences
in expression. We also attempted to determine how the number of genes rep
orted in the spatial data impacts the quality of the integrated data and o
f cell type mappings generated by our method. To address these questions\,
we analyzed both the provided seqFISH and scRNA-seq datasets\, as well as
additional scRNA-seq data (from the more recent Tasic visual cortex publi
cation) and STARmap data Code is available at https://github.com/jsodicoff
/birs_spatial_integration\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Singh Amrit
DTSTART:20200615T142000Z
DTEND:20200615T144000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/7
DESCRIPTION:Title: CORTEX seq-FISH: integration with scRNA-seq data\nby Singh
Amrit as part of Mathematical Frameworks for Integrative Analysis of Emer
ging Biological Data Types\n\n\nAbstract\nDr. Amrit Singh is a postdoctora
l fellow working with Professor Bruce McManus at the PROOF Centre of Excel
lence and The University of British Columbia and this work was done in col
laboration with Prof Kim-Anh Le Cao (University of Melbourne) . Dr. Amrit
Singh work addressed the question if scRNA-seq data be overlaid onto seqFI
SH for resolution enhancement? The published approach trained a multiclass
SVM on the scRNAseq data and applied it to the seqFISH data to estimate t
he cell-types labels. My approach uses a penalized regression method (glmn
et) with a semi-supervised approach in order to build a model using both t
he scRNAseq+seqFISH data. This strategy uses a recursive approach that inv
olves multiple rounds of training glmnet models using labeled data (label
and imputed) and predicting the cell-type labels of unlabeled data. At eac
h iteration\, cell-type labels with high confidence (probability > 0.5) ar
e retained for the next iteration\, where a new glmnet model is trained wi
th the scRNAseq data and seqFISH data with imputed cell-type labels with h
igh confidence. This process is repeated until all cell-types in the seqFI
SH data have been labeled or until 50 iterations have been reached (in ord
er to reduce compute times). The advantage of this approach is that more d
ata in used for model training such that the resulting model may generaliz
e better to new data. The performance of this approach was estimated using
cross-validation\, using only the scRNAseq data as the test set. Code is
available at https://github.com/singha53/ssenet\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bernd Bodenmiller
DTSTART:20200616T113000Z
DTEND:20200616T123000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/9
DESCRIPTION:Title: Keynote Talk (single cell proteomics)\nby Bernd Bodenmille
r as part of Mathematical Frameworks for Integrative Analysis of Emerging
Biological Data Types\n\n\nAbstract\nhttp://www.bodenmillerlab.com/ will p
resent recent work single cell proteomics work from his lab\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yingxin Lin
DTSTART:20200616T130000Z
DTEND:20200616T132000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/10
DESCRIPTION:Title: sc targeted proteomics: Predicting outcome\, survival from 3
proteomics datasets (Keren\, Jackson\, Wagner)\nby Yingxin Lin as part
of Mathematical Frameworks for Integrative Analysis of Emerging Biologica
l Data Types\n\n\nAbstract\nMs. Yingxin Lin is a PhD candidate in Statisti
cs under the supervision of Prof. Jean Yang\, Dr. John Ormerod and Dr. Rac
hel Wang at the University of Sydney. Her main research interest is in nor
malisation and statistical modelling of single-cell RNA-seq data. https://
yingxinlin.github.io/ She analyzed the three sc targeted proteomics datase
ts (Keren et al.\, Jackson et al.\, and Wagner et al.) which all presented
comprehensive portraits of breast cancer tumor immune microenvironment\,
utilizing different methods to identify and characterize different subtype
s of patients with the evidence associated with survival. Code is availabl
e at https://yingxinlin.github.io/BIRS_analysis\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chen Meng
DTSTART:20200616T132000Z
DTEND:20200616T134000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/11
DESCRIPTION:Title: sc targeted proteomics: comparing multi-block PCA\, linear re
gression\nby Chen Meng as part of Mathematical Frameworks for Integrat
ive Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Chen Men
g is Head of Bioinformatics at the Bavarian Center for Biomolecular Mass S
pectrometry\, TU Munich\, Freising\, Germany. (https://www.baybioms.tum.de
/about-us/people/) Dr. Chen Meng mainly worked approach integrating partia
lly overlapping proteomic data collected on different patients with simila
r phenotypes using two methods: simple linear regression (as a baseline/co
ntrol) and multi-block PCA (MBPCA\; including multiple co-inertia\, multip
le canonical correspondence analysis as special cases). In theory\, MBPCA
should outperform simple linear regression because it finds the correlated
pattern across multiple datasets\, preventing the potential problem of ov
erfitting to one dataset. Code is available at https://github.com/mengchen
18/BIRSBioIntegrationWorkshop\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pratheepa Jeganathan
DTSTART:20200616T134000Z
DTEND:20200616T135500Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/13
DESCRIPTION:Title: sc targeted proteomics:Stan model for latent Dirichlet alloca
tion\nby Pratheepa Jeganathan as part of Mathematical Frameworks for I
ntegrative Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. P
ratheepa Jeganathan received her masters (2013) and PhD (2016) from Texas
Tech University and is currently a postdoctoral research fellow working wi
th Prof Susan Holmes at Stanford University (https://profiles.stanford.edu
/pratheepa-jeganathan) Her work considered solutions for 1) how should we
approach integrating partially-overlapping proteomic data collected on dif
ferent patients with similar phenotypes? 2) Without including the spatial
x-y coordinate data\, how well can we predict cell co-location? She will i
llustrate the topic modeling on discretized targeted proteomics data and t
he method to infer cell co-location. We integrated the two SingleCellExper
iment using MultiAssayExperiment class in the R/Bioconductor package. We c
onverted the normalized data to original protein expression and discretize
d (for the preliminary analysis\, we added a minimum of the normalized val
ue for each marker\, but we need to know the sample mean and standard devi
ation of marker expressions in the MIBI data). We considered each cell is
a document and wrote a Stan model for latent Dirichlet allocation. Using p
osterior samples of topic proportions\, we inferred the latent topics with
a higher proportion in each cell. We proposed a solution to the alignment
issue. Code is available at https://github.com/PratheepaJ/Banff_proteomic
s\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Duncan Forster
DTSTART:20200616T144000Z
DTEND:20200616T150000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/14
DESCRIPTION:Title: Networks - learning salient gene and protein features from ne
twork topologies\nby Duncan Forster as part of Mathematical Frameworks
for Integrative Analysis of Emerging Biological Data Types\n\n\nAbstract\
nDr. Ducan Forster is postdoctoral fellow in Molecular Genetics co-supervi
sed by Prof Gary Bader and Charlie Brown at the University of Toronto. htt
ps://baderlab.org/Members His work has addressed the following questions.
Firstly\, we wanted to determine whether recent deep learning architecture
s (namely graph neural networks/graph convolutional networks) could be use
d to learn salient gene and protein features from network topologies. If s
o\, these features could be integrated in a trainable\, end-to-end fashion
allowing for effective integration of biological networks. These recent d
eep learning architectures have shown substantial improvements over previo
us network feature learning approaches on a range of tasks\, which motivat
es their use in biological domains. Secondly\, we wanted to determine more
effective evaluation strategies in order to compare integration approache
s. This is a challenging task due to differences in input network sizes an
d standard coverage\, biases and quality of the standards\, differences in
method outputs (networks vs. features)\, and biases in the current evalua
tion strategies themselves. Code is available at https://github.com/bowang
-lab/BIONIC\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Oliver Stegle
DTSTART:20200617T113000Z
DTEND:20200617T123000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/15
DESCRIPTION:Title: Keynote Talk (scNMT-seq study)\nby Oliver Stegle as part
of Mathematical Frameworks for Integrative Analysis of Emerging Biological
Data Types\n\n\nAbstract\nDr. Oliver Stegle is a group leader in Statisti
cal genomics and systems genetic at the European Bioinformatics Institute\
, Cambridge\, UK (https://www.ebi.ac.uk/research/stegle/) His lab publishe
d Argelaguet et al. 2019 Multi-omics profiling of mouse gastrulation at si
ngle-cell resolution Nature volume 576\, pages487–491(2019) https://www.
nature.com/articles/s41586-019-1825-8 which was the basis of the scNMT-seq
study challenge https://github.com/BIRSBiointegration/Hackathon/tree/mast
er/scNMT-seq\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Al J Abadi
DTSTART:20200617T130000Z
DTEND:20200617T132000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/16
DESCRIPTION:Title: scNMT-seq: multivariate integrative analyses\nby Al J Aba
di as part of Mathematical Frameworks for Integrative Analysis of Emerging
Biological Data Types\n\n\nAbstract\nDr. Al J Abadi is a Research Fellow
and software developer in Computational Genomics in the lab of Prof Kim-An
h Lê Cao at the University of Melbourne\, Australia (https://lecao-lab.sc
ience.unimelb.edu.au/) He addressed the challenges of i) Identification of
multi-omics signatures that characterize lineage\, stage or both: We appl
ied a regularised partial least square analysis which can find key markers
which characterize the coordinated lineage and stage-specific changes in
different modalities ii) Dealing with missing values: In integrative analy
ses\, we applied an iterative algorithm which can handle missing values wi
thout potentially inducing spurious correlations in the datasets while all
owing for select for variables that are correlated across data modalities
and characterize the stage and/or lineage of the cells\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joshua Welch
DTSTART:20200617T132000Z
DTEND:20200617T134000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/17
DESCRIPTION:Title: scNMT-seq: LIGER\nby Joshua Welch as part of Mathematical
Frameworks for Integrative Analysis of Emerging Biological Data Types\n\n
\nAbstract\nDr. Joshua Welch\, is Assistant Professor of Computational Med
icine and Bioinformatics in Department of Computational Medicine and Bioin
formatics\, University of Michigan (https://welch-lab.github.io/). Most re
cently\, his lab has focused on developing open-source software for the pr
ocessing\, analysis\, and modeling of single-cell sequencing data. Key con
tributions in this area include SingleSplice\, the first computational met
hod for single-cell splicing analysis\; SLICER\, an algorithm for inferrin
g developmental trajectories\; and LIGER\, a general approach for integrat
ing single-cell transcriptomic\, epigenomic and spatial transcriptomic dat
a. We used our previously published algorithm LIGER for this analysis. The
advantage of our method is that it can integrate different single-cell mo
dalities measured on different single cells. The corresponding disadvantag
e is that we do not leverage the known correspondence information from tru
e multi-omic measurements. We tried multiple data processing strategies fo
r the scNMT accessibility data. We observed limited alignment with all pro
cessing strategies\, but the more differentiated cell types showed more co
rrespondence. We also analyzed a different single-cell multi-omic dataset\
, SNARE-seq (RNA+ATAC) from mouse frontal cortex. LIGER was able to effect
ively integrate this dataset\, finding corresponding cell types between RN
A and ATAC data without using the known cell correspondences. We are furth
er investigating the possible biological and technical explanations for th
ese differences. Code is available https://github.com/jw156605/scNMT\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Arshi Arora
DTSTART:20200617T134000Z
DTEND:20200617T140000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/18
DESCRIPTION:Title: scNMT-seq:MOSAIC\, or Multi-Omic Supervised Integrative Clust
ering\nby Arshi Arora as part of Mathematical Frameworks for Integrati
ve Analysis of Emerging Biological Data Types\n\n\nAbstract\nArshi Arora i
s a Research Biostatistician in Dr. Ronglai Shen's lab at Memorial Sloan K
ettering Cancer Center\, https://www.mskcc.org/profile/arshi-arora Her res
earch addressed the following question\; We wish to address the problem of
identifying localized molecular signatures with respect to an outcome of
interest such as stage and lineage. This poses an interesting challenge in
understanding heterogeneity in cell populations across multiple data moda
lities. We aim to illustrate that the application of a supervised integrat
ive clustering will provide a more accurate delineation of cell subpopulat
ion across genomic\, epigenomic\, and transcriptomic landscape that is dir
ectly relevant to the biological outcome of interest. Code is available at
https://github.com/arorarshi/scNMT_seq_MOSAIC\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Wouter Meuleman
DTSTART:20200617T141000Z
DTEND:20200617T143000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/19
DESCRIPTION:Title: Integration sc chromatin : DNase-seq data across 733 biosampl
es\nby Wouter Meuleman as part of Mathematical Frameworks for Integrat
ive Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Wouter M
euleman is an investigator at the Altius Institute for Biomedical Sciences
. Wouter Meuleman’s research focuses on the organization of regulatory e
lements in the human genome and their relation to cellular state and gene
regulation. Prior to joining Altius\, Wouter did postdoctoral work at MIT
and the Broad Institute. He obtained his PhD in Computational Biology from
Delft University of Technology\, the Netherlands.(https://www.meuleman.or
g/) Dr Meuleman analyzed a large DNase I chromatin accessibility dataset (
733 biosamples) which are extremely rich and complementary to other commo
nly used data types. As such\, they are a perfect candidate for integrativ
e analyses. Although chromatin accessibility data result in rich genome-wi
de maps of putative regulatory elements\, these elements remain largely un
annotated and therefore these maps remain hard to use for downstream analy
ses. We wanted to provide a comprehensive annotation for each individual e
lement. Code is available at https://github.com/Altius/DHSVocabulary\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Susan Holmes
DTSTART:20200618T120000Z
DTEND:20200618T130000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/20
DESCRIPTION:Title: Keynote Talk: Computational Challenges\nby Susan Holmes a
s part of Mathematical Frameworks for Integrative Analysis of Emerging Bio
logical Data Types\n\n\nAbstract\nProfessor Susan Holmes is a Professor of
Statistics and member of BioX\, at Stanford University\, a John Henry Sam
ter University Fellow in Undergraduate Education\, a Fellow of the Fields
Institute. Moderator for the stat.AP arxiv. Slides at https://spholmes.git
hub.io/\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Love
DTSTART:20200618T133000Z
DTEND:20200618T140000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/21
DESCRIPTION:Title: Benchmarking\nby Michael Love as part of Mathematical Fra
meworks for Integrative Analysis of Emerging Biological Data Types\n\nAbst
ract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Vincent Carey
DTSTART:20200619T120000Z
DTEND:20200619T130000Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/22
DESCRIPTION:Title: Software Infrastructure\nby Vincent Carey as part of Math
ematical Frameworks for Integrative Analysis of Emerging Biological Data T
ypes\n\n\nAbstract\nVincent Carey is Professor of Medicine (Biostatistics)
in the Channing Division of Network Medicine\, Brigham and Women’s Hosp
ital\, Harvard Medical School. He is former Editor-in-Chief of The R Journ
al. He is Scientific Director of Bioinformatics in the National Institute
of Allergy and Infectious Diseases Immune Tolerance Network\, and is a mem
ber of the Scientific Advisory Board of the Vaccine and Immunology Statist
ical Center of the Collaboration for AIDS Vaccine Discovery. Vince is a co
-founder of the Bioconductor project.\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Elana Fertig
DTSTART:20200615T112500Z
DTEND:20200615T112900Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/23
DESCRIPTION:Title: CORTEX seq-FISH session\nby Elana Fertig as part of Mathe
matical Frameworks for Integrative Analysis of Emerging Biological Data Ty
pes\n\n\nAbstract\nDr. Fertig is a co-chair of this meeting and is an Asso
ciate Professor of Oncology and Assistant Director of the Research Program
in Quantitative Sciences at Johns Hopkins University\, with secondary app
ointments in Biomedical Engineering and Applied Mathematics and Statistics
\, affiliations in the Institute of Computational Medicine\, Center for Co
mputational Genomics\, Machine Learning\, Mathematical Institute for Data
Science\, and the Center for Computational Biology. Homepage: https://fert
iglab.com Twitter: @FertigLab\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aedin Culhane
DTSTART:20200616T110000Z
DTEND:20200616T110500Z
DTSTAMP:20260404T060944Z
UID:BIRS_20w5197/24
DESCRIPTION:Title: sc targeted proteomics session\nby Aedin Culhane as part
of Mathematical Frameworks for Integrative Analysis of Emerging Biological
Data Types\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/BIRS_20w5197/24/
END:VEVENT
END:VCALENDAR