TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations
Fig 1
The architecture of TranSynergy.
The input features include vector representations of Drug A, Drug B, and cell line vector, respectively. The first input dimension reduction component reduces the input dimension from 2401x3 to 512x3. The second component is a scaled dot product self-attention transformer. The third component is a fully connected neural network. Be noted that input matrix dimension changes to 2401x4 when both gene expression and gene dependency profiles are used for cell line representation.