Course
Deep Learning
Neural networks for images, sequences and structure.
Graph neural networks
- Can spreading signals through a protein network find disease genes?
- Can a known gene network make microarray classifiers interpretable?
- Can a graph neural network sort unknown phage DNA into families?
- Can self-supervised learning predict how mutations change binding?
- Does letting each node choose its own depth fix GNN over-smoothing?
- + 7 more →
Representation learning
- Can a small protein language model match much bigger ones?
- Can a transformer predict which drugs bind which proteins?
- Can one language model read protein sequence and structure?
- Can random negative pairs teach a better genome-sorting model?
- Can a self-taught CT model find cancer markers with little data?
- + 3 more →
Protein structure prediction
- Do short and long floppy protein regions need separate predictors?
- Can SVMs predict how membrane proteins sit in the membrane?
- Does combining predictors find more protein contacts?
- Can topology help neural nets predict how proteins behave?
- Can an SVM predict which amino acids touch in a folded protein?
- + 3 more →
Convolutional networks
- Can a neural network recognise which heart-ultrasound view it's seeing?
- Can ImageNet-trained networks read cancer tissue slides?
- Can a 3D image network predict how mutations change protein stability?
- Can a CNN predict how cancer cells respond to drugs?
- Can a text-style CNN read chemical formulas to predict toxicity?