Tripfly: Predicting Gene-gene Interaction of Drosophila Eye Development Using Triplet Loss

摘要

The reconstruction of gene regulatory network (GRN) is of significance in system biology. In recent years, benefiting from the advances of deep learning technologies, image-based gene expression data, which contains spatial expression patterns, has become a new resource in network inference. Most of the existing image-based GRN inference models are based on unsupervised models, due to the lack of labeled data. And a few methods employ supervised learning models, whose performance is limited by the scale of training data.In this study, in order to predict the gene regulatory network of the eye development of Drosophila embryos, we develop a weakly supervised learning method. We generate image triplets of genes according to their orientation and developing stage. Then we build a deep convolutional neural network, using triplet loss to train a siamese network and extract the relationship between genes. The new method achieves promising results in the prediction of gene regulatory relationship in the eye development of Drosophila with a total accuracy of over 72%.