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index.txt
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#+TITLE: Avoiding the pitfalls in L1-regularised inference of gene networks
#+EMAIL:
#+OPTIONS: reveal_center:t reveal_control:t reveal_height:-1
#+OPTIONS: reveal_history:nil reveal_keyboard:t
#+OPTIONS: reveal_mathjax:nil reveal_overview:nil reveal_progress:t
#+OPTIONS: reveal_rolling_links:nil reveal_slide_number:t
#+OPTIONS: reveal_width:-1 reveal_mathjax:t toc:nil
#+REVEAL_MARGIN: -1
#+REVEAL_MIN_SCALE: -1
#+REVEAL_MAX_SCALE: -1
#+REVEAL_ROOT: http://cdn.jsdelivr.net/reveal.js/2.6.2/
#+REVEAL_TRANS: linear
#+REVEAL_SPEED: default
#+REVEAL_THEME: simple
# this does not work for some reason. This does, but is not needed: <style> h1 { font-size: 2em !important; } </style>
# #+REVEAL_EXTRA_CSS: css/print/pdf.css
# ./css/custom.css
#+REVEAL_EXTRA_JS:
#+REVEAL_HLEVEL:
#+REVEAL_TITLE_SLIDE_TEMPLATE: <h2>%t</h2> <h3>%a</h3> <h4>%e</h4> <h4>%d</h4>
#+REVEAL_MATHJAX_URL: http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML
#+REVEAL_HEAD_PREAMBLE: <style> img { border-style: none !important; } </style> <meta name="apple-mobile-web-app-capable" content="yes" /> <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
#+REVEAL_PREAMBLE:
#+REVEAL_POSTAMBLE:
# #+REVEAL_PLUGINS:
* Systems Biology
#+reveal_html: <br>
#+reveal_html: <br>
GRN Network Inference
#+ATTR_HTML: :width 50%
file:./img/SysBio-2d.png
Inspired by /Arkin and Schaffer (2011)/
* Linear dynamical systems (LDS)
#+reveal_html: <br>
#+begin_src latex :exports results :results html
\[
\begin{array}{r c l}
\dot{x}_i(t) &=& \sum_{j=1}^N a_{ij}x_j(t) + p_i(t) - f_i(t)\\
y_i(t) &=& x_i(t) + e_i(t)
\end{array}
\]
#+end_src
#+reveal_html: <br>
#+reveal_html: <br>
at Steady State
#+begin_src latex :exports results :results html
\[
\boldsymbol{Y} = -\boldsymbol{A}^{-1}\boldsymbol{P} +\boldsymbol{A}^{-1}\boldsymbol{F} + \boldsymbol{E}
\]
#+end_src
* Solving LDS
#+reveal_html: <br>
#+reveal_html: <br>
#+ATTR_REVEAL: :frag roll-in
Regression
#+ATTR_REVEAL: :frag roll-in
Penalized regression
* The pitfalls of $L_1$
$L_1$ GRN inference methods fail even though the data is informative enough for inferring the correct network structure.
* Performance of $L_1$