演講者：王琪仁教授 (國立中正大學 數學系)


時間：9月23日上午11點
地點：A813教室

講題: Schloegl's second model on tree structure, and RNA secondary
structure prediction using machine learning methods

摘要: In the first half hour, we will introduce the Schloegl's
second model on a Bethe lattice when the coordination number z = 3. The
model involves spontaneous particle annihilation at rate p and
autocatalytic particle creation rate. Precise behavior for stochastic
models on regular periodic infinite lattices is usually surmised from
kinetic Monte Carlo simulation on a finite lattice with periodic
boundary conditions. However, the persistence of boundary effects for a
Bethe lattice complicates this process. We explored various boundary
conditions and unconventional simulation ensembles on the Bethe lattice
to predict behavior for infinite size. A discontinuous transition to the
vacuum state on the infinite lattice occurs when the annihilation rate p
around 0.053.

The remanding part of this talk will focus on the application of machine
learning on RNA secondary structure.
Our research applies the combinations of machine learning methods to
predict RNA secondary structure through Neural Network, Random Forest,
Extreme Gradient Boosting (XGBoosting), LightGBM (Light Gradient
Boosting Machine,LGBM), etc. The F1-score we obtained is around 0.914,
which is better than traditional prediction methods, but the newer deep
learning methods still perform (F1-score 0.937) better than us.