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