Chloé-Agathe Azencott

Associate Professor at the Centre for Computational Biology (CBIO) of Mines ParisTech, Institut Curie and INSERM.


Notes from JOBIM 2013

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JOBIM (Journées Ouvertes Biologie, Informatique et Mathématiques) is the French conference in bioinformatics. I attended the 14th edition in Toulouse, thanks to a travel fellowship from the conference that made it possible for me to travel from Germany and give a talk on network-guided multi-locus association mapping with graph cuts ([slides]).

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Installing PyGTK on Mac OS X 10.7

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This is how I installed PyGTK on my office machine, a Mac with OS X 10.7.5 (aka Lion) on which I've never managed to properly use fink or macports and gave up trying to install homebrew. In other words, without a package manager. Look, mommy, no hands!

I'm putting those notes here in case it might help someone struggling with similar problems. Please try using a package manager first, and save yourself some headache.

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Spring Travels

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I will be away from Tübingen in the next three weeks, attending SMILE in Paris on Monday, March 18th, as well as the Workshop in Computation, Inference and Optimization at IHÉS on Wednesday, March 20th in Bures-sur-Yvette (France).

I'm also looking very much forward to visiting EBI Cambridge at the end of the month. I will be giving a talk on network-guided multi-locus genome-wide mapping on Tuesday, March 27th at 11am.


Data Mining in Bioinformatics Course

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I will be teaching a few of the lectures in the course "Data Mining in der Bioinformatik" from February 18 to March 1st.

Lecture slides:


NIPS 2012

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I'll be attending NIPS next week, and am very much looking forward to what promises to be a great scientific week.

I will also be presenting a poster on my first results in graph-based feature selection[1] at the Machine Learning in Computational Biology workshop on December 7. I've been working with Dominik Grimm, Yoshinobu Kawahara and Karsten Borgwardt on the problem of finding single-point mutations that are maximally, jointly associated with an observed trait, while being connected in an underlying (predefined) biological network. We've been rather successful at dealing with the large (10^5 to 10^7) number of features involved, as in our experiments the method turns out to be fast, robust, and generally lead to better recall than our state-of-the-art comparison partner, the overlapping group lasso, for very similar precisions.

The method is currently called SOS for Subnetworks of Optimal SNPs, but I'm not very happy with the name and I'm considering renaming it SConES (Selection of Connected Explanatory SNPs).


[1] Although I have a lot of experience treating problems in which the objects themselves are represented by graphs (and the way they are connected is very much object dependent), I had never studied a setting in which the objects are not graph-like, but there is an underlying network that connecte their features (completely independently of the objects).

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