The development of single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has significantly advanced the study of cell heterogeneity in the epigenetic landscape. Numerous studies have leveraged scATAC-seq data to explore deeper gene regulatory relationships. However, scATAC-seq usually faces dropout events which may result in data sparsity and noise. In this work, we propose a method (scMCG) for analyzing scATAC-seq data that employs contrastive learning and a generative adversarial network (GAN). First, the scMCG method uses two distinct encoders for contrastive learning to solve the issues of feature redundancy and data sparsity in scATAC-seq data. Subsequently, a generator is used to reconstruct the latent embedding. Finally, a decoder is used to generate binary accessibility. We conduct experiments on multiple scATAC-seq datasets. The results demonstrate that the scMCG method achieves excellent performance in multiple tasks such as cell clustering and transcription factor activity influence.

Increasing evidences have highlighted the significant association between tsRNAs and diseases. Predicting potential tsRNA-disease associations based on computational methods can effectively reduce human and resource consumption. However, there is a scarcity of computational methods for predicting tsRNA-disease associations. Therefore, we propose Contrastive Learning-based prediction of tsRNA-Disease Associations (CLTDA). It reconstructs known associations between tsRNAs and diseases based on adaptive Singular Value Decomposition (SVD). Then, we employ Graph Convolutional Networks (GCNs) for feature extraction from both the original and reconstructed tsRNA-disease associations, and optimize the GCNs by using contrastive learning loss and Bayesian Personalized Ranking (BPR) loss. In addition, the Bayesian negative sampling method is used to select high-quality negative samples for learning the features of tsRNA and disease. Finally, a Multi-Layer Perceptron (MLP) is utilized to calculates the score of potential association. We conduct five-fold cross-validation and denovo experiments on a manually collected tsRNA-disease association dataset, and the experimental results show that CLTDA outperforms the other six state-of-the-art methods. We also perform a case study on lung cancer and experimental results show that CLTDA is an effective tool for predicting potential associations between tsRNAs and diseases.

The evolution of lung lesions can be assessed by examining multiple CT screenings, which needs to align two CT images accurately. In this study, we propose a multi-stride and multi-resolution affine registration network, called MMAR-net, for 3D affine registration of medical images, which works in an unsupervised way by optimizing the similarity loss. In order to extract more extensive image features, we use a multi-stride module to replace the conventional convolution module. Furthermore, we make use of the image features at multiple scales by dot product between two feature vectors, which could enhance the robustness of image representation. We conduct comprehensive comparison experiments between our model and the existing affine registration methods on two publicly available datasets, DIR-Lab and Learn2Reg, which are both relevant to lung CT image registration. Quantitative and qualitative comparison results demonstrate that our model outperforms existing single-step affine registration networks. Our method improves the key metric of dice similarity coefficient on DIR-Lab and Learn2Reg to 90.57% and 95.51%, respectively.