FusionModule.DNN

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class DNN(cell_dim: int, drug_dim: int, hid_dim_ls: list = None, dropout: float = 0.3, num_heads: int = 8,
pool: str = 'mean', concat: bool = True, classify: bool = False)

It can be used for fusion cell and drug feature to predict drug response.

PARAMETERS:

• cell_dim (int) - Dimension of cell feature.

• drug_dim (int) - Dimension of drug feature.

• hid_dim_ls (int, optional) - Dimension of hidden layers of DNN. (default: None)

  When the value is None, [512, 256, 128] is used.

• dropout (float, optional) - The dropout rate of DNN. (default: 0.3)

• num_heads (int, optional) - The number of heads of the multi-head attention network. (default: 8)

• pool (str, optional) - The pooling method of drug feature. (default: ‘mean’)

  The value could be ‘attention’, ‘mean’, ‘max’, ‘add’ or ‘mix’. It only takes effect when the drug feature is not ‘ECFP’.

• concat (bool, optional) - Whether to concat for fusion. (default: False)

  Cell and drug feature are added instead of concat when the value is True.

• classify (bool, optional) - Whether classification task. (default: False)

forward(f: torch.Tensor, x)

INPUTS:

• f (torch.Tensor) - The cell feature. [batch_size, cell_dim]

• x (torch.Tensor) - The drug feature.

  If the drug feature is ‘ECFP’ or ‘Image’, its shape is [batch_size, drug_dim]. If the drug feature is ‘SMILES’, its shape is [batch_size, drug_dim, seq_len]. If the drug feature is ‘Graph’, its format is (encoded_graph.x, graph).

OUTPUTS:

• f (torch.Tensor) - The predicted drug response. [batch_size, 1]

• cell_ft (torch.Tensor) - The cell feature. [batch_size, cell_dim]

• drug_ft (torch.Tensor) - The drug feature. [batch_size, drug_dim]