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Predicting icSHAPE: secondary structure probing data

methods:

We use window to convert RNA sequence into uniform slices for 1D deep learning model. We also try something new and fun: we convert one sequence to a 2D map and use specialized designed 2D U-net to predict it. (Later we find that google use the similar idea to predict SNP as images).

• 1D
  • CNN
  • RNN
  • ResNet
  • Seq2Seq
  • Attention
• 2D
  • U-net

Discover MOTIF: predict existence and location of motif

methods:

We revise and improve MEME’s EM algorithm to Mixture-PWM to make the model more robust to noises.

We also replace the PWM matrix with a Variational Auto-Encoder (VAE).

We then use Graph Convolutional Neural Network (GCN) to explore the possibility to predict Structural related motif. During our long time exploration, we find GCN may be the best method (in deep learning) to truly understand the structural information in RNA sequence.