Reconstructing high-precision gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data presents significant challenges, including high noise levels, data sparsity, and structural complexity. We introduce LGATFormer, a novel deep learning model that combines local structural modeling with global dependency extraction to address these challenges effectively. LGATFormer extracts enclosing subgraphs centered on target gene pairs, transforms them into line graphs, and uses Graph Attention Networks (GATs) to learn edge-level representations, capturing high-order regulatory structures. For global modeling, the model employs a Transformer encoder to process the entire gene expression matrix, utilizing self-attention mechanisms to model long-range dependencies between genes. Across multiple benchmarks, LGATFormer outperforms existing methods by achieving state-of-the-art AUROC and AUPRC on 89.29% of STRING and Non-Specific ground-truth networks, while exhibiting superior generalization, robustness, and interpretability. This model offers an effective and reliable solution for GRN inference, advancing both theoretical and practical applications in systems biology.

N4-acetylcytidine (ac4C) is a crucial RNA modification associated with mRNA stability and translational efficiency. Accurate identification of ac4C sites is essential for understanding their regulatory functions. However, experimental detection remains expensive and labor-intensive. At the same time, existing computational models suffer from limited generalization and insufficient feature discrimination, especially in distinguishing subtle nucleotide patterns. In this work, we propose a deep learning model named SNN-ac4C, which is based on a contrastive learning-based neural network. The model integrates a dual-path structure that combines BiLSTM and Multi-Head Self-Attention (MHSA) for capturing long-range dependencies and global context, while using CNN to extract local biological sequence features. The contrastive learning module further enhances the discriminative ability of ac4C and Non-ac4C sites by increasing the separation between positive and negative samples. Experiments on the test set confirm the effectiveness of SNN-ac4C, which achieves an accuracy (ACC) of 84.60% and a Matthews Correlation Coefficient (MCC) of 0.6934. Compared with NBCR-ac4C, the current state-of-the-art model, SNN-ac4C improves ACC and MCC by 1.09% and 0.0228, respectively. The source code and relevant supplementary are publicly available at https://github.com/2103374200/SNN.

Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.

One of the trends in the evolution of Android malware is the increasing diversity of malicious behaviors, such as SMSrelated and Internet-related actions. Traditional binary or familybased classification methods are inadequate for fine-grained detection of these behaviors. Thus, multi-label classification is required to identify various malicious behaviors within a single malware sample. This paper employs an active learning strategy to add multi-behavior labels to large-scale datasets based on expert-annotated small-scale datasets. To address the issue of noisy labels (simulating real-world mislabeling), we propose CalmDroid, an active learning framework utilizing the coreset strategy, instead of the confuse-set strategy for updating the model with out-of-distribution (OOD) points. We evaluate CalmDroid's performance using the Drebin and VirusShare datasets. Experimental results demonstrate that CalmDroid achieves superior detection performance under varying noise conditions, with an accuracy improvement of up to 0.704 compared to the confuse-set strategy. In high-noise environments (15%), it reaches detection accuracy as high as 0.944. Additionally, we validate CalmDroid's capability to detect evolving malware. Despite behavioral evolution in Drebin malware across different time steps, CalmDroid consistently achieves detection rates above 70 % in the newest time step.

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. Code is available at https://github.com/711L/PEFN.

Commercial microwave links (CMLs) are used to transmit information between base station towers in cellular networks. Opportunistic remote sensing of rainfall, using the signal level measurements from CMLs, has proven to be highly accurate in rain rate estimation. As traditional CML link has single antenna and polarization setup, its capability of retrieving other precipitation parameters is limited. The latest microwave technology line-of-sight multiple-input multiple-output (LoS MIMO) are equipped with multiple transmitters and receivers to increase capacity. In a 2x2 LOS-MIMO system, each of the two antennas employs a different polarization. In this study, we investigate the potential of using LOS-MIMO microwave link to retrieve parameters related to rain drop size distribution based on measurement data.

With high accuracy and stability of GPS radio occultation data in the stratosphere, COSMIC data is used to validate the Microwave Temperature Sounder (MWTS) channel 4 (57.29GHz, shorted for ch4) brightness temperature (TB) measures on FY3A/3B in 2011-2012. We also introduce AMSU-A TB observations on NOAA satellites to further investigate the TB differences and their possible causes. Finally, a correction method is proposed for MWTS ch4. The results show that the differences between observed (O) and simulated (M) TB of MWTS ch4 are more significant than the corresponding AMSU-A differences. The distributions of TB differences (O-M) between AMSU-A and MWTS are similar at high latitudes; at mid-latitudes and the tropics MWTS observations are significantly higher than not only the simulated TB, but also the AMSU-A observations, and in particular in the tropics, the TB measurements at MWTS ch4 are significantly higher than AMSU-A ch9 by 1-5 K, which suggests that the discrepancy is mainly due to the data quality of MWTS ch4 itself. Comparisons of the MWTS ch4 TB before and after using correction method show that the correction is most effective in the tropics and the mid-latitudes, especially in the tropic the significantly higher TB deviation of about 2-3 K was reduced to 0-1 K after the correction, and the corrected TB differences are reduced in all four latitude zones.

Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved. In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization. First, the preprocessed Dataset 1 and Dataset 2 are transformed into the form of images. The proposed MDNDO-SMDU resampling technique is then used to balance the number of samples in each category in the training dataset. Finally, deep transfer learning was used to construct the predictive model MSlocPRED to identify subcellular localization for 16 classes (Dataset 1) and 18 classes (Dataset 2). The results of comparative tests of different resampling techniques show that the resampling technique proposed in this study is more effective in preprocessing for subcellular localization. The prediction results of the datasets constructed by intercepting different NC end (Both the 5' and 3' untranslated regions that flank the protein-coding sequence and influence mRNA function without encoding proteins themselves.) lengths show that for Dataset 1 and Dataset 2, the prediction performance is best when the NC end is intercepted by 35 nucleotides, respectively. The results of both independent testing and five-fold cross-validation comparisons with established prediction tools show that MSlocPRED is significantly better than established tools for identifying multi-label mRNA subcellular localization. Additionally, to understand how the MSlocPRED model works during the prediction process, SHapley Additive exPlanations was used to explain it. The predictive model and associated datasets are available on the following github: https://github.com/ZBYnb1/MSlocPRED/tree/main.

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes. The modulation encompasses inhibiting cancer cell growth, delaying ovarian aging, regulating metabolic diseases, and ameliorating depression. Consequently, the identification and comprehension of post-translational lysine modifications hold substantial value in the realms of biological research and drug development. Post-translational modifications (PTMs) at lysine (K) sites are among the most common protein modifications. However, research on K-PTMs has been largely centered on identifying individual modification types, with a relative scarcity of balanced data analysis techniques. In this study, a classification system is developed for the prediction of concurrent multiple modifications at a single lysine residue. Initially, a well-established multi-label position-specific triad amino acid propensity algorithm is utilized for feature encoding. Subsequently, PreMLS: a novel ClusterCentroids undersampling algorithm based on MiniBatchKmeans was introduced to eliminate redundant or similar major class samples, thereby mitigating the issue of class imbalance. A convolutional neural network architecture was specifically constructed for the analysis of biological sequences to predict multiple lysine modification sites. The model, evaluated through five-fold cross-validation and independent testing, was found to significantly outperform existing models such as iMul-kSite and predML-Site. The results presented here aid in prioritizing potential lysine modification sites, facilitating subsequent biological assays and advancing pharmaceutical research. To enhance accessibility, an open-access predictive script has been crafted for the multi-label predictive model developed in this study.

N4-methylcytosine (4mC) is one of important epigenetic modifications in DNA sequences. Detecting 4mC sites is time-consuming. The computational method based on machine learning has provided effective help for identifying 4mC. To further improve the performance of prediction, we propose a Laplacian Regularized Sparse Representation based Classifier with L2,1/2-matrix norm (LapRSRC). We also utilize kernal trick to derive the kernel LapRSRC for nonlinear modeling. Matrix factorization technology is employed to solve the sparse representation coefficients of all test samples in the training set. And an efficient iterative algorithm is proposed to solve the objective function. We implement our model on six benchmark datasets of 4mC and eight UCI datasets to test evaluate performance. The results show that the performance of our method is better or comparable.

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in some specific cell types. Importantly, DGRNS identifies a series of regulatory relationships with high confidence and functional consistency, which have not yet been experimentally confirmed and merit further research.

Lots of biological processes are controlled by gene regulatory networks (GRNs), such as growth and differentiation of cells, occurrence and development of the diseases. Therefore, it is important to persistently concentrate on the research of GRN. The determination of the gene-gene relationships from gene expression data is a complex issue. Since it is difficult to efficiently obtain the regularity behind the gene-gene relationship by only relying on biochemical experimental methods, thus various computational methods have been used to construct GRNs, and some achievements have been made. In this paper, we propose a novel method MMFGRN (for "Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction") to reconstruct the GRN. In order to make full use of the limited datasets and explore the potential regulatory relationships contained in different data types, we construct the MMFGRN model from three perspectives: single time series data model, single steady-data model and time series and steady-data joint model. And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life.

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://github.com/Not-so-middle/iEnhancer-KL.git.

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ24, σ28, σ32, σ38, σ54and σ70. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at 90.05% and 80.13%. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

MOTIVATION: N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed. RESULTS: In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail. AVAILABILITY AND IMPLEMENTATION: The web-server 4mCPred is accessible at http://server.malab.cn/4mCPred/index.jsp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The integrated Precipitable Water (PW) derived from ground IGS stations is considered as reference because of its high accuracy and long-term stability to validate the PW derived from COSMIC RO sounding data. The one-year comparisons show that both PW are quite consistent for PW below 20 mm, and the PW derived from COSMIC has systemic low bias than ground-GPS PW for PW >40 mm. In general, the rms of PW difference between COSMIC and IGS is about 1.5-3.0mm, and the mean difference is less than 0.5mm, which is acceptable. In addition, the variations of PW difference in the southern hemisphere are obviously fluctuant due to fewer IGS stations over this region, while the mean PW difference is more stable and smaller around 0.5 mm for latitude higher than 25°N and the standard deviation of PW difference is gradually decreases with higher latitude in the northern hemisphere.