• Calendar

March 9, Wednesday
12:00 – 13:30

RNA molecules are fundamental participants in many biological processes, where recent discoveries revile that the role of RNA is much more important than was previously known. A key property for analyzing the functionality of RNA is its structure. Due to their size, it is difficult to determine structures of RNA molecules physically, and thus computational RNA structure prediction (a.k.a "RNA folding") is an important bioinformatic application.

In this talk, we present a new approach which significantly improves the accuracy of RNA folding. The method combines rich feature representations of RNA molecules with a light-weight machine learning algorithm (the "online passive aggressive" algorithm), and obtains about 50% reduction of error rate with respect to the best previous result.