Saturday, September 28, 2019

Re:(9) Business offer

To unsubscribe, send us an email with the subject 'unsubscribe' zBjE
We offer e-mail databases at affordable prices. zBjE
For marketing, advertising, newsletters. zBjE
This is the most effective way to attract customers for your business. zBjE
Country: Number of e-mail addresses: zBjE
US : 5 million zBjE
RU : 8 million zBjE
DE : 8 million zBjE
AU : 3 million zBjE
CA : 3 million zBjE
UK : 6 million zBjE
FR : 3 million zBjE
NZ : 1 million zBjE
COM 55 million zBjE
ALL WORLD 250 million zBjE

In addition, we can provide a base for any country in the world. zBjE

Our contacts: zBjE
7753191 zBjE

Wednesday, September 18, 2019


Good day to you.

We sent you an email yesterday about foundation project funding.

Do let me know if you received it so further details can be advised.

kindest regards,
fred Anderson

Saturday, September 14, 2019



My name is Mr E. Hagler, and I have an investment management proposal for you, for more details, reply to this email immediately.

Thank you for your understanding.

Sincerely Yours,

Eugen Hagler

Tuesday, September 3, 2019


I am Mrs. Francisca Lister, Your email it still valid?
Please write Fred Anderson on or call his direct telephone number +447759708698 for more details your grant of two million Five Hundred Thousand Euros.
Best Regards,
Mrs. Francisca Lister
London United Kingdom

Tuesday, August 6, 2019

Loan Offer...Apply Now

Dear Sir/Madam,

This is to inform you that our company is presently offering loans to interested persons at an affordable rate with the help and assistance from our banks.

We Offer Long And Short Term Loans to individuals and companies.Are you contemplating a business venture, seeking loans,funding or capital to finance existing hotels, office buildings, multi-family projects, other commercial project?

Do you need a LOAN for Business or you need a personal Loan. You can apply for a fast loan here and get it with ease. We can raise up to 500 Million United States Dollars to finance different kinds of projects.

For more details regarding our LOAN offer,you are advised to contact our office today via Email and provide the following:

Your Name:
Home Address/Country:
Mobile Telephone Number:
Loan Amount:
Loan Duration:

We are proud to say that, despite global economic uncertainty, we are still one of the World's fastest growing independent finance companies.

Contact us today for your Business/Personal Loans.

Respectfully Submitted,

Sarah Marconi
Financial/Loan Consultant

Friday, May 31, 2019

Abstract for talk at the MRC LMB Cambridge

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 found that 
differences in the proportions of cell types explain >85% of the 
cross-population variation. 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. 



Abstract for talk at the UNIVERSITY OF BERN


My talk will focus on how to leverage thousands of functional genomics datasets to deeply annotate the disease genome and perform data mining to discover disease-associated regulators and variations.


First, I will introduce our computational efforts to perform large-scale integration to accurately define distal and proximal regulatory elements (MatchedFilter) and then show how our extended gene annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend.


Second, I will look at our comprehensive regulatory networks of both transcription factors and RNA-binding proteins (TFs and RBPs). I will showcase their value by highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of the well-known oncogenic TF MYC.


Third, I will introduce a workflow to prioritize key elements and variants. I will showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS (LARVA, MOAT & uORF tools). Targeted validations of the prioritized regulators, elements and variants demonstrate the value of our annotation resource.


Finally, I will put all these methods together through application to kidney and prostate cancers.