Explore
Home 
Literature 
Links 
Posts 
Molecules 
Blogs 
Zeitgeist 
Markup Help 
News 
Everything Papers Books
We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

Posts

Let the machine learning wars commence! That’s my impression on reading over the situation I’m detailing today, at any rate. This one starts with this paper in Science, a joint effort by the Doyle group at Princeton and Merck, which used ML techniques...
Let the machine learning wars commence! That’s my impression on reading over the situation I’m detailing today, at any rate. This one starts with this paper in Science, a joint effort by the Doyle group at Princeton and Merck, which used ML techniques...