Jiangwen Sun

TL;DR: This study provides a comprehensive examination of gene activities in bovine embryos and identified little-known potential master regulators of pre-implantation development, demonstrating that bovines are better models for human embryonic development.

TL;DR: The proposed GCN model, which is called edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution, and exploits correspondence between bonds in different molecules.

TL;DR: Zhang et al. as mentioned in this paper proposed a novel approach for view imputation via generative adversarial networks (GANs), which they named by VIGAN, which first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views.

TL;DR: A new approach is proposed to identify and correct mislabeled training instances by employing a Bayesian classifier and information entropy calculated from the probability distributions to evaluate the typicality of the instance belonging to considered class labels.

TL;DR: The studies show that TB associated with human embryos is in rapid developmental flux during peri-implantation period when it must invade, signal robustly to the mother to ensure that the pregnancy continues, and make first contact with the maternal immune system.