I'd like to introduce you one of the faces of the project: Alex, the genomics rat idol.
Arguably, Alex is the most popular rat on the internet. For the justification of this strong statement, read on.
Alex was born in May 2000. It's well known that a rat's cuteness reaches maximum at about 3-4 weeks. After this critical time, a pet store rat is less likely to be purchased and may be asked to act as snake food. In Alex's case, she was perilously close to her deadline. Luckily for her, we paid a ransom of $6.99 to the Noah's Ark pet shop in Vancouver. She was on her last cute leg.
From May 2000 Alex spent most of her time hoarding food pellets and riding on shoulders.
Alex liked to bite. And rats only bite hard — they don't nibble. Her contention for this unattractive behaviour was the uncanny similarity between a finger and a pellet of food.
Other than unpredictable bouts of biting (by far the most exciting aspect of her personality), Alex lacked other distinguishing characteristics.
Alex died of a seizure in late 2002. She was buried outside of the Museum of Anthropology. A ratty pair of underwear served as a burial shroud.
And I hope you got that last pun.
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Despite my best efforts at meaningful work, this web page continues to be the most popular of all my online offerings, making for a somewhat embarrassing achievement.
Alex's images consistently show up first in Google's web search for 'rat', 'rat image' and image search for 'rat'.
Finally, Alex appears as the first entry in Google images for 'rat'.
Alex's Public Appearances
More recently, she's appeared on the cover of Ethnologie Francaise (Jan-Mar 2009 issue).
The topic of the issue was the relationship between animals and humans. It is fitting therefore to recount here the relationship I shared with Alex during her sojourn with us.
Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.
Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.
We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.
For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.
Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.
Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.
Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.