The invention belongs to the field of
bioinformatics, and relates to an improved integrated
deep learning cell communication ligand-
receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and
dimensionality reduction on biological sequence features of a ligand and a
receptor, and constructing multi-
modal feature input; secondly, constructing an improved deep neural network
branch, introducing a batch normalization layer and a Leaky ReLU
activation function, solving the problems of gradient disappearance and neuronal
necrosis, and improving regularization strength to prevent
overfitting; meanwhile, an enhanced heterogeneous graph auto-
encoder branch is constructed, the
graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the
prediction probability of a heterogeneous graph auto-
encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a
complex cell communication network.