Lung cancer continues to be the leading cause of cancer-related deaths globally. Early detection and diagnosis of pulmonary nodules are essential for improving patient survival rates. Although previous research has integrated multimodal and multi-temporal information, outperforming single modality and single time point, the fusion methods are limited to inefficient vector concatenation and simple mutual attention, highlighting the need for more effective multimodal information fusion. To address these challenges, we introduce a Dual-Graph Spatiotemporal Attention Network, which leverages temporal variations and multimodal data to enhance the accuracy of predictions. Our methodology involves developing a Global-Local Feature Encoder to better capture the local, global, and fused characteristics of pulmonary nodules. Additionally, a Dual-Graph Construction method organizes multimodal features into inter-modal and intra-modal graphs. Furthermore, a Hierarchical Cross-Modal Graph Fusion Module is introduced to refine feature integration. We also compiled a novel multimodal dataset named the NLST-cmst dataset as a comprehensive source of support for related research. Our extensive experiments, conducted on both the NLST-cmst and curated CSTL-derived datasets, demonstrate that our DGSAN significantly outperforms state-of-the-art methods in classifying pulmonary nodules with exceptional computational efficiency.

Super-enhancers (SEs), comprising clusters of transcriptional regulatory elements, play essential roles in gene expression regulation and cell fate determination. Current computational methods for identifying SEs from genomic sequences face challenges in prediction accuracy and cross-species generalizability. This study introduces TransSE, a deep learning framework combining convolutional and recurrent neural networks with cross-species transfer learning for SE prediction. TransSE employs a two-phase strategy: pre-training on combined human and mouse data to learn conserved regulatory features, followed by species-specific fine-tuning to capture organism-specific patterns. Evaluation on human and mouse datasets demonstrates that TransSE achieves superior accuracy compared to existing methods including SENet, DeepSE, and transformer-based models (DNABERT, Enformer), with AUC values of 0.828 (human) and 0.832 (mouse). Ablation studies confirm that convolutional blocks are essential for feature extraction, while transfer learning and recurrent layers provide meaningful performance improvements. Feature analysis reveals that TransSE distinguishes SEs from typical enhancers based on learned sequence representations, achieving 10.4-fold improvement in clustering metrics. The model demonstrates effective cross-species prediction capability, maintaining AUC $> $ 0.79 when trained on one species and evaluated on another. Conservation pattern analysis shows that TransSE integrates both evolutionarily conserved and species-specific regulatory features, enabling robust performance across organisms with limited training data. A user-friendly web interface (http://transse.shenlabahmu.com) provides automated SE prediction, motif analysis, and variant impact assessment. Case studies using experimentally validated SEs demonstrate the tool's utility for investigating regulatory elements and prioritizing functional variants in disease-associated genomic regions. TransSE advances the investigation of gene regulatory mechanisms and facilitates identification of disease-associated variation in enhancer regions.

Enhancer-enhancer interactions (EEIs) are critical regulatory components in transcriptional networks but remain computationally challenging to predict. While enhancer-promoter interactions have been extensively studied, EEIs remain comparatively underexplored. We developed ETNet (Enhancer-enhancer Interaction Explainable Transformer Network), a deep learning architecture integrating convolutional neural networks with Transformer modules to predict EEIs from DNA sequences. Evaluation across three cell lines (GM12878, K562, MCF-7) demonstrated superior performance compared to existing methods including EnContact, with statistical significance confirmed through DeLong tests across six cell lines. Rigorous validation through cross-validation and enhancer-level data partitioning confirmed robust generalization. ETNet exhibited effective cross-cell type transfer learning and showed transferability to enhancer-promoter interaction tasks, providing exploratory evidence for shared chromatin interaction principles. Feature attribution analysis recovered cell-type-specific regulatory motifs consistent with known transcription factors and revealed computational evidence for super-additive cooperative mechanisms, with cooperativity negatively correlating with sequence similarity-patterns representing hypothesis-generating observations requiring experimental validation. Proof-of-concept analysis demonstrated how single-nucleotide polymorphisms in JAK-STAT pathway genes may influence predicted interactions through motif alterations. ETNet advances computational approaches for studying enhancer interactions and provides a framework combining predictive capability with exploratory interpretability.

Open chromatin regions (OCRs) allow direct interaction between cis-regulatory elements and trans-acting factors. Therefore, predicting all potential OCR-mediated loops is essential for deciphering the regulation mechanism of gene expression. However, existing loop prediction tools are restricted to specific anchor types. Here, we present CharID (Chromatin Accessible Region Interaction Detector), a two-step model that combines neural network and ensemble learning to predict OCR-mediated loops. In the first step, CharID-Anchor, an attention-based hybrid CNN-BiGRU network is constructed to discriminate between the anchor and nonanchor OCRs. In the second step, CharID-Loop uses gradient boosting decision tree with chromosome-split strategy to predict the interactions between anchor OCRs. The performance was assessed in three human cell lines, and CharID showed superior prediction performance compared with other algorithms. In contrast to the methods designed to predict a particular type of loops, CharID can detect varieties of chromatin loops not limited to enhancer-promoter loops or architectural protein-mediated loops. We constructed the OCR-mediated interaction network using the predicted loops and identified hub anchors, which are highlighted by their proximity to housekeeping genes. By analyzing loops containing SNPs associated with cardiovascular disease, we identified an SNP-gene loop indicating the regulation mechanism of the GFOD1. Taken together, CharID universally predicts diverse chromatin loops beyond other state-of-the-art methods, which are limited by anchor types, and experimental techniques, which are limited by sensitivities drastically decaying with the genomic distance of anchors. Finally, we hosted Peaksniffer, a user-friendly web server that provides online prediction, query and visualization of OCRs and associated loops.

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

Analog signals are more sensitive to crosstalk than digital signals, resulting in instability of analog circuits. To eliminate coupling, it is common practice to insert shielding wires on one or both sides of critical signals. In this paper, a novel analog circuit shielding routing algorithm based on net classification is proposed. Circuit performance requirements are transformed into geometric properties of nets according to the result of placement, and different shielding wire routing algorithms are designed to meet these geometric properties. A* algorithm is adopted to route the critical nets, and shielding wires are added at the same time. Maze algorithm is used to route the P/G nets and other general nets. Experimental results show that the router is efficient in routing and effective in reducing crosstalk. Although capacitive load and routing area increase, the resulting coupling is negligible and the circuit performance is significantly improved.

In this paper, a novel design-for-manufacture-aware detailed routing algorithm that seeks to minimize the thickness range of the chip surface after copper damascene process is proposed. The paper is based on an electroplating (ECP) and chemical mechanical polishing (CMP) model and predictors for final thickness range are abstracted. The proposed detailed routing is implemented in a W-shape multilevel full-chip routing framework using depth first search and branch-and-bound techniques in maze backtracking. Experimental results show that compared to maze routing (MR) (that does not consider CMP), the improvements in the average metal density standard and the average amount of dummy fill are 12.0% and 6.99% respectively. Compared to density-driven maze routing (DMR) that considers only CMP but does not consider ECP, the improvements in the average metal density standard and the average amount of dummy fill are 0.53% and 0.72%, respectively. So, the proposed algorithm can obtain improvement in optimizing CMP while the wire length and vias are not increased clearly and the completion rate is guaranteed. Therefore, the yield of chips is improved.