Spatial transcriptomics (ST) provides groundbreaking opportunities to study biological processes and disease mechanisms by measuring gene expression profiles and spatial coordinates. Accurate spatial domain identification via effective data representation is crucial for biological discovery. We propose a multi-context modeling framework with spatial adaptive enhancement (SAE) for ST data analysis. The SAE algorithm leverages spatial information to denoise and enhance expression data, eliminating the adverse impacts of high noise and sparsity inherent in ST data. Given the enhanced data, we incorporate diverse sample context relationships, including spatial, expression, and random non-neighbor contexts, via masked attention. This multi-context strategy overcomes the reliance of current methods on local neighbors, further enhancing the expressiveness of sample embeddings. A graph Laplacian loss preserves sample adjacency in latent space, ensuring spatial coherence of the identified domains. Experiments on five public datasets demonstrate that our method outperforms seven state-of-the-art benchmarks in domain identification and robustness. As a plug-and-play algorithm, SAE significantly improves benchmarks' performance, highlighting its broad application potential.

Single-cell multi-omics technologies have revolutionized the study of cell states and functions by simultaneously profiling multiple molecular layers within individual cells. However, existing methods for integrating these data struggle to preserve critical feature information and fail to exploit known regulatory knowledge, which is essential for understanding cell functions. This limitation hinders their ability to provide comprehensive and accurate insights into cells. Here, we propose FactVAE, an innovative factorized variational autoencoder designed for the robust and accurate understanding of single-cell multi-omics data. FactVAE integrates the factorization principle into the variational autoencoder framework, ensuring the preservation of feature information while leveraging the non-linear capture of sample information by neural networks. Additionally, known regulatory knowledge is incorporated during model training, and a knowledge transfer strategy is employed for cell embedding optimization and data augmentation. Comparative analyses of single-cell multi-omics datasets from different protocols and the spatial multi-omics dataset demonstrate that FactVAE not only outperforms benchmark methods in clustering performance but also generates augmented data that reveals the clearest cell-type-specific motif expression. Moreover, the feature embeddings captured by FactVAE enable the inference of potential and reliable gene regulatory relationships. Overall, FactVAE's superior performance and strong scalability make it a promising new solution for single-cell multi-omics data analysis.

Place recognition is an important task for robots and autonomous cars to localize themselves and close loops in pre-built maps. While single-modal sensor-based methods have shown satisfactory performance, cross-modal place recognition that retrieving images from a point-cloud database remains a challenging problem. Current cross-modal methods transform images into 3D points using depth estimation for modality conversion, which are usually computationally intensive and need expensive labeled data for depth supervision. In this work, we introduce a fast and lightweight framework to encode images and point clouds into place-distinctive descriptors. We propose an effective Field of View (FoV) transformation module to convert point clouds into an analogous modality as images. This module eliminates the necessity for depth estimation and helps subsequent modules achieve real-time performance. We further design a non-negative factorization-based encoder to extract mutually consistent semantic features between point clouds and images. This encoder yields more distinctive global descriptors for retrieval. Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time. Additional evaluation on the HAOMO dataset covering a 17 km trajectory further shows the practical generalization capabilities. We have released the implementation of our methods as open source at: https://github.com/haomo-ai/ModaLink.git.

The moth-flame optimization (MFO) algorithm is extensively employed to attain the global optimization of the problem. The disadvantages of the original MFO algorithm include poor population variety, a sluggish rate of convergence, and an easy propensity to be drawn in by local optimum. This paper presents a bi-population cooperative moth-flame optimization algorithm (BCMFO) to address the issues. Utilizing the low discrepancy sequence (LDS), a random population with a uniform distribution is produced in the search space. Two subpopulations with similar sizes are updated using Gauss mutation and opposition learning. To improve the algorithm's ability to search globally, the elite method is used to eliminate the subpar solutions from the population. BCMFO is used to optimize the networking mode and is confirmed using the benchmark test suite in CEC 2017. Experimental results show that BCMFO outperforms state-of-the-art algorithms.

This paper investigates a traveling salesman problem (TSP), which has important applications in real-world scenarios. A reinforcement learning-driven iterated greedy algorithm (RLIGA) is presented to address the TSP. A population initialization method based on the famous FRB2 heuristic is proposed to generate an initial population with high quality. To enhance the effectiveness of the RLIGA, the local search method and the destruction-construction mechanisms are designed for the city sequence. A generation method of sub-population based on current population sequence information is proposed to generate sub-population. An acceptance criterion is proposed to determine whether the offspring are adopted into the population. A re-destruction and re-construction method is proposed to avoid the proposed algorithm falling into local optimum. Lastly, the RLIGA is tested on the TSPLIB benchmark instances. The experimental results show that RLIGA is an effective algorithm to address the problem.

BACKGROUND: In the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information. RESULTS: We use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets. METHOD: This paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity. CONCLUSION: Compared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the “curse of dimensionality” and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm’s variation process to balance the algorithm’s search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated.

In recent decades, the rapid development of gene sequencing and computer technology has increased the growth of high-dimensional microarray data. Some machine learning methods have been successfully applied to it to help classify cancer. In most cases, high dimensionality and the small sample size of microarray data restricted the performance of cancer classification. This problem usually issolved bysome feature selection methods. However, most of them neglect the exploitation of relations among genes. This paper proposes a novel feature selection method by fusing multiple gene relation network information based on community detection (MGRCD). The proposed method divides all genes into different communities. Then, the genes most associated with cancer classification are selected from each community. The proposed method satisfies both maximum relevances gene with cancer and minimum redundancy among genes for the selected optimal feature subset. The experiment results show that the proposed gene selection method can effectively improve classification performance.

BACKGROUND: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes. RESULTS: Experimental results show that the proposed method is superior to classical algorithms and advanced methods in feature number and accuracy, and the selected features have more significance. METHOD: This paper proposes a feature selection method based on a graph neural network. The proposed method uses the actual dependencies between features and the Pearson correlation coefficient to construct graph-structured data. The information dissemination and aggregation operations based on graph neural network are applied to fuse node information on graph structured data. The redundant features are clustered by the spectral clustering method. Then, the feature ranking aggregation model using eight feature evaluation methods acts on each clustering sub-cluster for different feature selection. CONCLUSION: The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.

The discovery of diagnostically significant genes from microarray data is essential for disease diagnosis and drug research. However, the difficulty of analyzing microarray data comes from its high dimensionality and small sample size. Feature selection can effectively remove irrelevant and redundant features, reduce data dimensionality, and improve the accuracy of classifiers. This paper proposes a two-stage hybrid feature selection method MMBDE based on the improved min-Redundancy and Max-Relevance (mRMR) and the improved Binary Differential Evolution (BDE) algorithm. The improved mRMR is used to reduce the feature dimensionality at a coarse-scale significantly. In contrast, the improved BDE is used to refine the feature dimensionality at fine-scale further and select the best features. The experimental results show that MMBDE successfully reduces the dimensionality of microarray gene expression data, obtains high classification accuracy, and extracts effective features closely related to diseases from microarray gene expression data. The relevant datasets and codes can be obtained from https://github.com/xwdshiwo/MMBDE.

BACKGROUND: Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. RESULTS: The proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data. METHOD: In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering. CONCLUSION: The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.