Saturday, September 28, 2019

Re:(9) Business offer

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Wednesday, September 18, 2019

18/09/2019

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
Email:wbfefft@aim.com

Saturday, September 14, 2019

Sir/Madam

Sir/Madam,

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

hello

Hello,
I am Mrs. Francisca Lister, Your email it still valid?
Please write Fred Anderson on fd.27@aol.com 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

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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. 


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i0lon19-mrc



Abstract for talk at the UNIVERSITY OF BERN

Abstract: 


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.


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i0lon19-bern