The cell cycle plays a pivotal role in regulating cell fate and stem cell differentiation. As a rate-limiting step in differentiation, its precise regulation is essential for maintaining cellular diversity and tissue homeostasis. Recent advances in single-cell multi-omics technologies have enabled the integration of gene expression data and chromatin structural regulation, thereby enhancing the prediction of cell cycle using multi-omics approaches. However, current algorithms have yet to effectively integrate transcriptome and three-dimensional (3D) genomic data for cell cycle prediction. We propose MomicPred, an innovative dual-branch multi-modal fusion framework designed to predict cell cycle dynamics. This framework integrates transcriptome-derived gene expression data with global chromatin structural insights from 3D genome data. By leveraging the complementary nature of these multi-omics data, MomicPred extracts three core feature sets that uncover cross-layer associations and synergistic interactions between the two omics modalities, enabling high-precision cell cycle prediction. We further evaluate the framework's performance through various benchmarking strategies, demonstrating its efficiency and robustness. Furthermore, feature importance analysis reveals chromatin structural changes and key biological processes across distinct cell cycle stages, offering new perspectives for future research.

Choosing an appropriate division method is crucial for partitioning two-dimensional spatial data under the constraints of differential privacy. The current mainstream partitioning methods include grid-based partitioning and hierarchical partitioning. In order to optimize query accuracy while satisfying differential privacy conditions, it remains challenge to achieve the sum minimization of noise error and uniformity assumption error. To address this issue, we propose the HOLG (Hierarchical Optimization of Logical Grids) algorithm, employing a ”divide-merge-divide” approach. It begins with fine-grained grid partitioning of the data domain, followed by heuristic merging of grids with similar data distributions. After determining the scale of the query domain, the merged regions are further subdivided into smaller regions with similar query probabilities, constructing a hierarchical structure to reduce uniformity assumption errors. Additionally, we design a novel noise injection method and introduce consistency constraints to further minimize noise errors. To reduce the time complexity of the HOLG partitioning method, Huffman trees is employed to optimize the processing of the hierarchical tree set generated by HOLG, ensuring query utility while effectively reducing the query response time for the partitioning algorithm. Experimental results on large-scale spatial datasets demonstrate that HOLG outperforms similar algorithms in query accuracy. Furthermore, when combined with the Huffman tree optimization, it effectively reduces query response time.

The widespread use of smartphones has yielded a wealth of behavioral sequence data from user interactions. These interactions offer insights into user preferences and patterns for personalized behavior prediction. However, there are some challenges in current privacy-preserving works for analyzing these data. These approaches have suboptimal service quality with smaller but longer datasets and insufficient emphasis on secure pattern storage and retrieval in real-world applications. To handle these challenges on smartphones, we propose a novel Privacy-enhanced Frequent Sequence Mining and Retrieval (PrivFSMR) framework for this scenario. Specifically, we first introduce a dynamic sequence truncation to anonymize the maximum sequence length of datasets. Following this, we design a privacy-enhanced FSM algorithm to uncover patterns, effectively reducing the privacy budget by integrating differential privacy and the Markov assumption. During the secure pattern storage, PrivFSMR employs symmetric encryption for protection and constructs an encrypted index forest for retrieval. Lastly, future behavior retrieval leverages current device information and the index forest to search similar patterns, thereby predicting potential user behaviors in the future. A comprehensive security analysis proves the PrivFSMR framework guarantees differential privacy and maintains storage and retrieval confidentiality in the lifecycle. In addition to using two publicly available datasets, we also collected a real behavior dataset within 2-4 weeks from 30 users for evaluation. Experimental results on three datasets demonstrate that PrivFSMR excels in mining frequent patterns and predicting future behaviors compared to existing approaches.

Single-cell Hi-C technology is utilized to obtain chromosome interaction information at the single-cell level and further study the differences in genome structures between different cell types. However, there are few accurate and efficient clustering methods for single-cell Hi-C data, with the following manifestations: The clustering efficacy on the dataset with a large number of cells is not very satisfactory, and it is difficult to cluster these cells with small number in the whole dataset. In this study, we propose a high-performance single-cell Hi-C clustering framework, called scHiCSC. A new smoothing method based on contact number weight is first proposed to generate cell embedding with more accurate cell features. In addition, a novel feature fusion method is proposed to further supplement the feature information of cells by fusing the chromosome structure information within cells and the distance information between cells. The experimental results show that scHiCSC has a strong generalization ability on different sizes of datasets and outperforms the existing single-cell Hi-C clustering frameworks. Moreover, scHiCSC achieves an optimal and stable clustering efficacy in the datasets with large-scale cell numbers and can cluster the cells with small number in the whole dataset. The source code of scHiCSC is freely available at https://github.com/HaoWuLab-Bioinformatics/scHiCSC.