Good day

Good day,

The Iraq Ministry of Trade is actively seeking interested global service partners for key rebuilding and development projects inIraq.

Iraq is changing rapidly and as life is returning to normal, so too is economic activity. Demand for goods and services are growing, and the country is proving why many believe it to be one of the world's most attractive markets.

Iraq offers almost unparalleled opportunities to international corporations and investors. At the same time, Iraq can finally benefit from foreign investment into its economy. Iraq's needs are broad it ranges across all sectors.

I would like to know if you and your company will like to participate or help facilitate the urgent delivery of products and services to key development sectors of the Iraqi economy in all the provinces.

I work as a special international trade consultant with the Iraq Ministry of Trade, and my primary responsibility is to seek and connect partners and investors with the Iraq Ministry of Trade for profitable projects that address Iraq's needs and to ensure that the environment exists for simple entry into the Country.

Regards,
Rimarzik Matthias Ernst

Re: Thank you!

TITLE: Genomics & Data Science

ABSTRACT:

Data science is currently popular because it allows the extraction of
useful information and commercial value from large-scale datasets.
Here I will describe biomedical data science, particularly from the
framework of genomics, which is one of the leading sub-fields under
the data science umbrella. First, I will talk about the increase in
data generation and data availability in this discipline, and how
genomics has developed methods to cope with data growth, and how these
methods interrelate with data science in general. Then, I'll go
through a number of key applications of biomedical data science,
particularly in relation to drugs and therapeutics, and focus on
finding drug targets for neuropsychiatric disease. This application of
data science makes extensive use of biological regulatory networks and
I will show how one can analyze these networks, in terms of analogies
to social networks and hierarchies.

Fwd: Your upcoming Special Cell Circuits & Epigenomics seminar/visit on April 1st

TITLE:

Personal Genomics & Data Science

In this seminar, I will discuss issues in personal genome analysis.
In particular, I will focus on
disorders of the brain, which affect nearly a fifth of the world's
population. Robust phenotype-genotype associations have been
established for a number of brain disorders including psychiatric
diseases (e.g., schizophrenia, bipolar disorder). However,
understanding the molecular causes of brain disorders is still a
challenge. To address this, the PsychENCODE consortium generated
thousands of transcriptome (bulk and single-cell) datasets from 1,866
individuals. Using these data, we have developed a set of
interpretable machine learning approaches for deciphering functional
genomic elements and linkages in the brain and psychiatric disorders.
In particular, we deconvolved the bulk tissue expression across
individuals using single-cell data via non-negative matrix
factorization and non-negative least squares and found that
differences in the proportions of cell types explain >85% of the
cross-population variation observed. Additionally, we developed an
interpretable deep-learning model embedding the physical regulatory
network to predict phenotype from genotype. Our model uses a
conditional Deep Boltzmann Machine architecture and introduces lateral
connectivity at the visible layer to embed the biological structure
learned from the regulatory network and QTL linkages. It
improves disease prediction (by 6-fold compared to additive polygenic
risk scores), highlights key genes for disorders, and allows
imputation of missing transcriptome information from genotype data
alone.

In the second half of the talk, if there's time, I'll discuss various data
science issues in drug design, in particular in developing a predictor that
gives one's differential sensitivity to a drug, taking into account his or her
personal variants.

==
i0brd19

12~特邀~您聆~取 188 圆~用扣扣Q903401978惘zhi⑥0⑷957佃c-om

澳<我的眼睛怎样地从眼眶跃出，>菛威\<可怜一个人对于幸福太容易上瘾了！等到自私的幸福变成了人生唯一的>尼澌<嫩牛拖犁耙不打不跑>6<饶人算之本，输人算之机。>9<久住令人贱，频来亲也疏。>2<硬把它当作私人游乐的花园？>00<真正的朋友是一个灵魂寓于两个身体，两个灵魂只有一个思想，两颗心跳动是一致的。>24<乞丐跳舞穷快活 青竹竿掏茅坑越掏越臭 >、<凡事豫(预)则立,不豫(预)则废。(《礼记》)>c<谁也不能将阳光装进自己的口袋。谁也不能将真理霸占。>0m特<都具有一种灵活强劲的保证，>邀您住<对你唱着："你独身就一切皆空。">冊嶺㈡<粘米煮山芋糊糊涂涂 >㈩㈣<习与正人居之，不能无正也；犹生长于齐，不能不齐语也。>瀛<墙上栽花高种(中)>10<使你抛弃了我反而得到光荣：>0<人才虽高，不务学问，不能致圣。>缇現<心是灵魂的眼睛，而不是力量的源泉>

RE: Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Thank you, Mark! Yes, this will be a great topic for the audience. Look forward to meeting you next month. Feel free to email or contact me should you need any further help. My cell phone is 317.622.8881.

Jake

-----Original Message-----
From: Mark Gerstein <mark@gersteinlab.org>
Sent: Thursday, February 14, 2019 7:07 PM
To: Chen, Jake Y <jakechen@uab.edu>
Cc: glabstracts.mbglab@blogger.com
Subject: Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Talk Title:

Brain Genomics

Abstract:

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with
>88% reconstruction accuracy). It also allows the building of a gene
regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

URL:

resource.psychencode.org

Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Talk Title:

Brain Genomics

Abstract:

Despite progress in defining genetic risk for psychiatric disorders,
their molecular mechanisms remain elusive. Addressing this, the
PsychENCODE Consortium has generated a comprehensive online resource
for the adult brain across 1866 individuals. The PsychENCODE resource
contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and
topologically associating domains; single-cell expression profiles for
many cell types; expression quantitative-trait loci (QTLs); and
further QTLs associated with chromatin, splicing, and cell-type
proportions. Integration shows that varying cell-type proportions
largely account for the cross-population variation in expression (with
>88% reconstruction accuracy). It also allows the building of a gene
regulatory network, linking genome-wide association study variants to
genes (e.g., 321 for schizophrenia). We embed this network into an
interpretable deep-learning model, which improves disease prediction
by ~6-fold versus polygenic risk scores and identifies key genes and
pathways in psychiatric disorders.

URL:

resource.psychencode.org

Fwd: Request for Information and Travel to the Sigma Xi Annual Meeting

BIG DATA AND THE FUTURE OF RESEARCH SYMPOSIA
October 26–28, 2018
Hyatt Regency San Francisco Airport, California
#SigmaXimtg
https://www.sigmaxi.org/meetings-events/annual-meeting-and-student-research-conference/big-data-symposia

Posted & "tweeted" my talk, viz:
https://twitter.com/MarkGerstein/status/1055636248956690432
(I'd use the ppt.)

Talk title and abstract

"Personal Genomics & Data Science"

In this seminar, I will discuss issues in transcriptome analysis. I
will first talk about some core aspects - how we analyze the activity
patterns of genes in human disease. In particular, I will focus on
disorders of the brain, which affect nearly a fifth of the world's
population. Robust phenotype-genotype associations have been
established for a number of brain disorders including psychiatric
diseases (e.g., schizophrenia, bipolar disorder). However,
understanding the molecular causes of brain disorders is still a
challenge. To address this, the PsychENCODE consortium generated
thousands of transcriptome (bulk and single-cell) datasets from 1,866
individuals. Using these data, we have developed a set of
interpretable machine learning approaches for deciphering functional
genomic elements and linkages in the brain and psychiatric disorders.
In particular, we deconvolved the bulk tissue expression across
individuals using single-cell data via non-negative matrix
factorization and non-negative least squares and found that
differences in the proportions of cell types explain >85% of the
cross-population variation observed. Additionally, we developed an
interpretable deep-learning model embedding the physical regulatory
network to predict phenotype from genotype. Our model uses a
conditional Deep Boltzmann Machine architecture and introduces lateral
connectivity at the visible layer to embed the biological structure
learned from the regulatory network and QTL linkages. Our model
improves disease prediction (by 6-fold compared to additive polygenic
risk scores), highlights key genes for disorders, and allows
imputation of missing transcriptome information from genotype data
alone. In the second half of the talk, I will look at the "data
exhaust" from transcriptome analysis - that is, how one can find
additional things from this data than what is necessarily intended.
First, I will focus on genomic privacy: How looking at the
quantifications of expression levels can potentially reveal something
about the subjects studied, and how one can take steps to protect
patient anonymity. Next, I will look at how the social activity of
researchers generating transcriptome datasets in itself creates
revealing patterns in the scientific literature.
==
i0sigma