Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play pivotal roles in various human diseases. Predicting associations such as lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs) is crucial for understanding disease mechanisms and identifying therapeutic targets. However, existing models face significant challenges in handling extreme data imbalance and often treat multiple ncRNA-disease and ncRNA-ncRNA interactions collectively, lacking the ability to provide precise, differentiated predictions for specific types of ncRNAs. This limitation reduces their practical applicability. To address these issues, we propose the Dual Balanced Augmented Topological Noncoding RNA Disease triplet Association (DBATNDA) model. DBATNDA constructs an Interaction Dual Graph with LDAs, MDAs, and LMIs as nodes and introduces an efficient graph-based balanced topological augmentation mechanism to enhance node structural representation and adaptability to imbalanced data. This innovative approach enables fast and accurate predictions of ncRNA-disease and ncRNA-ncRNA triplet associations through node classification view. To the best of our knowledge, no existing method employs such a dual-representation strategy to provide simultaneously differentiated predictions for the associations between diverse ncRNAs and diseases while also enhancing target specificity. Experimental results demonstrate DBATNDA's superior performance compared to state-of-the-art models, while case studies confirm its practical significance in these triple association prediction. The code and datasets are publicly available at https://github.com/AI4Bread/DBATNDA.

Single-cell ATAC-seq (scATAC-seq) technology allows the interrogation of chromatin accessibility of individual cells. Dropout events occur while the sequencing data signals at some bona fide chromatin sites of individuals are not captured, and the curse of these dropouts in scATAC-seq data inevitably hinders downstream analysis. It remains a challenge to impute scATAC-seq data due to its high dimensionality, sparsity, and near-binarization properties. Herein, we propose IMATAC, a deep hierarchical network with denoising autoencoder for imputing scATAC-seq data in the form of peak by cell. The network embeds scATAC-seq data into a latent space by a deep hierarchical architecture at two different levels, including bottom level for local details and top level for global information, that helps to characterize the high-dimensional sparse scATAC-seq data. Besides, it is encouraged to learn to reconstruct the original scATAC-seq data from an artificially corrupted version through a denoising autoencoder, so as to acquire an ability to recover the missing values primarily relying on the cells under the same population with the help of a parallel multi-classifier. Using simulated and experimental data, the performance of IMATAC is demonstrated by a comparative analysis with the other competing methods. The results suggest that our method can achieve lower imputation errors, and benefit the downstream analysis, including heterogeneous clustering, differential analysis, and regulatory element discovery. Besides, the contributions of several important network modules in our IMATAC are investigated, and how well it can separate the dropout zeros from biological zeros are discussed.

Single-cell Hi-C (scHi-C) technology enables probing of higher-order chromatin structures in individual cells. It provides an opportunity to get a deeper insight into genomic compartmentalization changes of single cells across different conditions, paving the way to a common understanding of the interplay among compartmental organization, genome functions, and cellular phenotypes. Unfortunately, there are only a few methods currently available for the differential analysis of A/B compartments on Hi-C data at the bulk level; the computational analysis of compartmentalization changes at the single-cell level is a field in its infancy. Herein, we propose DeepExDC, an interpretable 1D convolutional neural network for differential analysis of A/B compartments in scHi-C data on a genome-wide scale. It accepts Hi-C contact matrices at the single-cell level, runs without any distribution assumption and differential pattern limitation, and interprets genomic compartmentalization changes across multiple conditions. The results on simulated and experimental scHi-C data show that our DeepExDC has higher accuracies in detecting different types of compartmentalization changes, and the interpretation values are demonstrated to be able to reflect compartment changes across cell types. It is also observed that the differential compartments given by DeepExDC agree well with those by state-of-the-art methods at the bulk level, help to characterize heterogeneity of single cells, and exhibit a reasonable biological relevance in multiple regards. In addition, considering that DeepExDC is free of distribution assumptions and differential patterns, we attempted to transfer it onto scRNA-seq and scATAC-seq data; it is interesting that our method also presents considerable power compared with the competing methods.

Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics. We propose ACLNDA, an asymmetric graph contrastive learning framework for analyzing heterophilic ncRNA-disease associations. It constructs inter-layer adjacency matrices from the original lncRNA, miRNA, and disease associations, and uses a Top-K intra-layer similarity edges construction approach to form a triple-layer heterogeneous graph. Unlike traditional works, to account for both node attribute features (ncRNA/disease) and node preference features (association), ACLNDA employs an asymmetric yet simple graph contrastive learning framework to maximize one-hop neighborhood context and two-hop similarity, extracting ncRNA-disease features without relying on graph augmentations or homophily assumptions, reducing computational cost while preserving data integrity. Our framework is capable of being applied to a universal range of potential LDA, MDA, and LMI association predictions. Further experimental results demonstrate superior performance to other existing state-of-the-art baseline methods, which shows its potential for providing insights into disease diagnosis and therapeutic target identification. The source code and data of ACLNDA is publicly available at https://github.com/AI4Bread/ACLNDA.

MOTIVATION: Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity. RESULTS: We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels. AVAILABILITY AND IMPLEMENTATION: scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.

CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep learning ignore the ability of the model to explicitly extract local depth information of the CDA. We propose a model based on knowledge graph from recursion and attention aggregation for circRNA-disease association prediction (KGRACDA). This model combines explicit structural features and implicit embedding information of graphs, optimizing graph embedding vectors. First, we built large-scale, multi-source heterogeneous datasets and construct a knowledge graph of multiple RNAs and diseases. After that, we use a recursive method to build multi-hop subgraphs and optimize graph attention mechanism by gating mechanism, mining local depth information. At the same time, the model uses multi-head attention mechanism to balance global and local depth features of graphs, and generate CDA prediction scores. KGRACDA surpasses other methods by capturing local and global depth information related to CDA. We update an interactive web platform HNRBase v2.0, which visualizes circRNA data, and allows users to download data and predict CDA using model.

The prediction of unsteady flow field is a challenge in the area of fluid dynamics due to the spatiotemporally dynamic behavior. Given high-dimensional spatial information of the unsteady flow field for a number of previous time steps, the subsequent multi-step or even multi-cycle information is to be obtained. Inspired by human intuitive physics, we investigate a purely visual approach for unsteady flow field prediction directly from images which are obtained by mapping physical quantities to the color space, and propose a novel stochastic latent variable model based on deep neural networks. Our model is evaluated on two numerical simulation datasets of different complexities in terms of conditions and scopes of the flow field. The results show that trained with two-cycle data, over ten-cycle future flow fields are accurately predicted by our model on the low-complexity dataset. For the high-complexity one, our model also gives accurate multi-cycle predictions after trained with data of more cycles.

MOTIVATION: The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse. RESULTS: We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices. It provides a workflow consisting of imputation and embedding, graph construction, dual graph refinement, pseudotime calculation and result visualization. Beyond the few existing methods, scHiCPTR ties to optimize graph structure by two parallel procedures of graph pruning, which help reduce the spurious cell links resulted from noises and determine a global developmental directionality. Besides, it has an ability to handle developmental trajectories with multiple topologies, including linear, bifurcated and circular ones, and is competitive with methods developed for single-cell RNA-seq data. The comparative results tell that our scHiCPTR can achieve higher performance in pseudotime inference, and the inferred developmental trajectory exhibit a reasonable biological significance. AVAILABILITY AND IMPLEMENTATION: scHiCPTR is freely available at https://github.com/lhqxinghun/scHiCPTR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example.