Abstract for Applied Data Science talk [i0ds2]

TITLE:

Transcriptome Mining:
Tackling core issues related to gene regulation
& also analyzing the "data exhaust" associated with this activity

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 model organisms and humans. I will focus on how
we cluster these patterns together, finding conserved modules across
diverse species, and then, how we analyze the regulation of these
modules, determining whether their dynamics is driven solely
internally or involves an external agent. 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.

http://papers.gersteinlab.org/papers/dreiss
http://papers.gersteinlab.org/papers/orthoclust
http://papers.gersteinlab.org/papers/privaseq