Dpt Computational Biology Methods Primer “Foundation models of RNAseq data and Biological Networks” – Jeremie Kalfon

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Dpt Computational Biology | Methods Primer “Foundation models of RNAseq data and Biological Networks” – Jeremie Kalfon

2025-01-20

  • Modeling ScRNAseq
    • Gene network, via machine learning
    • Transformer » Neural Network (NN) (e.g., AE, ScVI)
    • Input matrix, col - cells, rows - genes.
    • Different type of methods:
      • graph based
      • decomposition
      • MF
  • NN
    • ReLU: Non linearity
    • Stochastic gradient descent
  • Auto encoder
    • Dimension reduction, works like PCA?
  • ScVI
    • Loss function: $L_{VAE} = \prod{SE} + \beta KL(p\vert\vert q)$, $p$ is prior, $q$ is a prediction.
  • Transformer
    • linking input matrix to output matrix
    • Inside: SA, MLP
    • skip connection
    • attention matrix
    • good for scaling
    • SAs (self attention): $softmax(XQK^TX^T/\sqrt{d_k})XV$
  • GPT
    • There is encoder and decoder but it is not the same as those mentioned above.
    • Maybe it can help to find link between genes, forming the gene network.
    • Order of genes can be identified.
  • DNA transformer