Jiayang Chen

TL;DR: This work proposes a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning and discovers that the pre-trained RNA-FM could infer sequential and evolutionary information of non-Coding RNAs without using any labels.

TL;DR: The first end-to-end deep learning approach, E2Efold-3D, is developed to accurately perform the de novo RNA structure prediction and achieves promising results when predicting RNA complex structures, a feat that none of the previous systems could accomplish.

TL;DR: Wang et al. as mentioned in this paper presented a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and downstream analysis, which overcomes the heterogeneity of the experimental data with a specifically designed representation learning task.

TL;DR: Extensive experiments show Contact-Distil outperforms previous state-of-the-arts by large margins on CAMEO-L dataset for low homologous PCMP, i.e., around 13.3% and 9.5% improvements against Alphafold2 and MSA Transformer when MSA count less than 10.

TL;DR: It is demonstrated that combining unlabeled medical images with coarse segmentation results can effectively improve the robustness of the lesion segmentation model and a practical framework for improving the performance of medical image segmentation given limited labeled data samples is proposed.