Sunday, March 31, 2019

Re: Thank you!

TITLE: Genomics & Data Science


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.

Saturday, March 23, 2019

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


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

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.


Tuesday, March 12, 2019

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

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