Recent advancements in single-cell sequencing technologies enable the measurement of multiple modalities in individual cells, offering insights into the transcriptome and regulome in various biological systems and human diseases in an unprecedented resolution. However, effectively using these ultra-high-dimensional and large-scale multiomic data to understand gene regulation remains challenging. Inspired by the success of adapting large language models into the genomics field, we develop scMBERT, a BERT framework-based pre-trained deep learning model using single-cell multiomic data. We showed that scMBERT increases model flexibility and performance in downstream tasks like cell type annotation and batch-effect correction, demonstrating the potential of leveraging multiomic data to improve single-cell genomic data analyses.

ABSTRACT Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by gradual cognitive decline and structural brain degeneration. Magnetic resonance imaging (MRI), due to its non‐invasive nature and high spatial resolution, plays a pivotal role in the clinical diagnosis of AD. However, considerable challenges persist, primarily due to the heterogeneity of brain structural alterations across individuals and the high computational burden associated with deploying deep learning models in clinical practice. Although recent deep learning‐based approaches have significantly improved diagnostic accuracy, most models fail to identify the specific contributions of individual brain regions, limiting their interpretability and clinical applicability. To address these limitations, we propose LGL‐Net, a novel lightweight 3D convolutional neural network tailored for efficient extraction and integration of both global and local anatomical features from MRI data. The architecture adopts a dual‐branch design, wherein one branch captures whole‐brain atrophy patterns, while the other focuses on fine‐grained, region‐specific structural variations. This design achieves a favourable trade‐off between computational efficiency and diagnostic performance, significantly reducing the model's parameter count and computational load without compromising accuracy. Importantly, LGL‐Net explicitly maps learnt features onto anatomically defined brain regions, enabling region‐level interpretability of classification outcomes. By independently evaluating the contributions of each region to both global and local representations, the model elucidates how multiscale anatomical features collectively influence diagnostic decisions. Experimental results demonstrate that LGL‐Net achieves classification performance comparable to existing methods, while substantially lowering model complexity and computational demands. Overall, this framework offers a scalable, interpretable and resource‐efficient solution for intelligent AD diagnosis.

This paper employs an active clamping module (ACM) to balance voltages of series-connected IGBTs in a medium voltage (MV) voltage source converter (VSC), which has been used for SiC MOSFETs. The principles of charging and discharging within the ACM are briefly introduced, revealing that the ACM capacitors have less charging current compared with series-connected SiC MOSFETs due to tail current. This process is modeled, leading to guidelines for selecting appropriate ACM capacitance. Design considerations for a MV VSC are discussed. Preliminary back-to-back tests on IGBT half-bridge modules were conducted at a DC voltage level of 400V and an AC current of 150A, confirming the effectiveness of the proposed analysis and design.

SUMMARY: Emerging single-cell technologies (e.g. single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Single-cell regulome data are highly sparse and discrete. Analyzing such data is challenging. User-friendly software tools are still lacking. We present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g. gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations. AVAILABILITY AND IMPLEMENTATION: SCRAT is freely available at https://zhiji.shinyapps.io/scrat as an online web service and at https://github.com/zji90/SCRAT as an R package. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In recent years, more 3D protein structures have become available, which has made the analysis of large molecular structures much easier. There is a strong demand for geometric models for the study of protein-related interactions. Alpha shape and Delaunay triangulation are powerful tools to represent protein structures and have advantages in characterizing the surface curvature and atom contacts. This review presents state-of-the-art applications of alpha shape and Delaunay triangulation in the studies on protein-DNA, protein-protein, protein-ligand interactions and protein structure analysis.

Mutations in EGFR kinase domain can cause non-small-cell lung cancer, which is one of the most lethal diseases in the world. However, current therapy is limited by the drug resistance effect in different EGFR mutants. There is an urgent demand for developing computational methods to predict drug resisted mutations. In this study, we use quantum mechanics and molecular mechanics models to generate EGFR mutants, and apply molecular dynamic to simulate EGFR-drug interactions. Hydrogen bonds and binding free energy are used to reveal the underlying principle of drug resistance in EGFR. The results show that drug resisted mutants do not establish hydrogen bond between the drug and the protein molecule while having large binding free energy. These properties can be used to predict resistance to anti-EGFR drugs due to protein mutations.

Interactions between biomolecules play an essential role in various biological processes. For predicting DNA-binding or protein-binding proteins, many machine-learning-based techniques have used various types of features to represent the interface of the complexes, but they only deal with the properties of a single atom in the interface and do not take into account the information of neighborhood atoms directly. This paper proposes a new feature representation method for biomolecular interfaces based on the theory of graph wavelet. The enhanced graph wavelet features (EGWF) provides an effective way to characterize interface feature through adding physicochemical features and exploiting a graph wavelet formulation. Particularly, graph wavelet condenses the information around the center atom, and thus enhances the discrimination of features of biomolecule binding proteins in the feature space. Experiment results show that EGWF performs effectively for predicting DNA-binding and protein-binding proteins in terms of Matthew's correlation coefficient (MCC) score and the area value under the receiver operating characteristic curve (AUC).

Protein-ligand interaction is important in drug development and protein design. Previous studies focused on the detection of the potential ligand binding sites but lack of analysis of the true binding sites on the protein. In this work, we use alpha shape models to represent the surface of the protein structure and extract surface patches to study the ligand binding sites. Analysis is performed on X-ray crystallography data of protein-ligand structures. The results show that geometric properties play a significant role in the positioning of ligand molecule in protein-ligand interaction.

Previous studies about protein-DNA interaction focused on the bound structure of DNA-binding proteins and provided good but not practical results. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and use structural alignment to develop an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of unbound DNA-binding protein. The proposed method provides good performance in predicting unbound structure of DNA-binding protein which is potentially useful in many fields. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve sensitivity ranges from 48.08% to 44.23% and specificity ranges from 73.82% to 84.29%.

MOTIVATION: Protein-DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein-DNA complex are becoming available, the surface characteristics of the complex become an important research topic. RESULT: In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein-DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein-DNA interactions. AVAILABILITY: The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm CONTACT: [email protected].