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  1. A cross model single-cell atlas reveals conserved involvement of osteopontin in polycystic kidney disease
  2. GARP+ Erythroid Regulatory Cells (G-ERCs) define spatially organized immunosuppressive niches that restrain anti-tumor immunity 2259805
  3. An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid
  4. SpaFlow depicts the dynamics of ligand-receptor interaction in spatial transcriptomics data
  5. ZFP148 is a transcriptional repressor of cytolytic effector CD8+ T cell differentiation
  6. A multi-agent platform for assessment and improvement of bioinformatics software documentation
  7. Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging
  8. Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT
  9. Epigenetic Modulation, Intratumoral Microbiome, and Immunity in Early-Onset Colorectal Cancer
  10. Heightened protein synthesis and aggregation, coupled with futile protein quality control, underlie a novel mechanism of CD8+ T cell exhaustion 3479
  11. ZFP148 is a transcriptional checkpoint for effector CD8+ T cell differentiation 3939
  12. Proteotoxic stress response drives T cell exhaustion and immune evasion
  13. Ad hoc, post hoc and intrinsic-hoc in bioinformatics
  14. Advancing biological understanding of cellular senescence with computational multiomics
  15. Data from Tumor-associated NK Cells Regulate Distinct CD8<sup>+</sup> T-cell Differentiation Program in Cancer and Contribute to Resistance against Immune Checkpoint Blockers
  16. Supplementary Figures S1-S16 from Tumor-associated NK Cells Regulate Distinct CD8<sup>+</sup> T-cell Differentiation Program in Cancer and Contribute to Resistance against Immune Checkpoint Blockers
  17. Supplementary Tables S1-S4 from Tumor-associated NK Cells Regulate Distinct CD8<sup>+</sup> T-cell Differentiation Program in Cancer and Contribute to Resistance against Immune Checkpoint Blockers
  18. The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes
  19. Analysis of head and neck cancer scRNA-seq data identified PRDM6 promotes tumor progression by modulating immune gene expression
  20. TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues
  21. O-GlcNAc transferase plays dual antiviral roles by integrating innate immunity and lipid metabolism
  22. Characterizing Cellular Heterogeneity and Transcriptomic Features of Senotype Using Deep Graph Representation Learning
  23. Tumor-associated NK Cells Regulate Distinct CD8+ T-cell Differentiation Program in Cancer and Contribute to Resistance against Immune Checkpoint Blockers
  24. TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues
  25. 934 ZFP148 represses effector differentiation and cytotoxicity of CD8+ T cells during chronic viral infection and cancer
  26. Graph Fourier transform for spatial omics representation and analyses of complex organs
  27. Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
  28. A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD)
  29. Graph Fourier transform for spatial omics representation and analyses of complex organs
  30. Data from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  31. Data from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  32. FIGURE 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  33. FIGURE 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  34. FIGURE 2 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  35. FIGURE 2 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  36. FIGURE 3 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  37. FIGURE 3 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  38. Supplementary Figure 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  39. Supplementary Figure 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  40. Supplementary Table 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  41. Supplementary Table 1 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  42. Supplementary Table 2 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  43. Supplementary Table 2 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  44. Supplementary Table 3 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  45. Supplementary Table 3 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  46. Supplementary Table 4 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  47. Supplementary Table 4 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  48. Supplementary Table 5 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  49. Supplementary Table 5 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  50. Supplementary Table 6 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  51. Supplementary Table 6 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  52. Supplementary Table 7 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  53. Supplementary Table 7 from A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  54. A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset
  55. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
  56. A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)
  57. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
  58. Computational methods and challenges in analyzing intratumoral microbiome data
  59. A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset
  60. An explainable graph neural framework to identify cancer-associated intratumoral microbial communities
  61. A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data
  62. Single-cell biological network inference using a heterogeneous graph transformer
  63. Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction
  64. NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health
  65. Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
  66. Spatial omics representation and functional tissue module inference using graph Fourier transform
  67. Machine learning development environment for single-cell sequencing data analyses
  68. 943 Harnessing anti-tumor metabolic sensing switch GPR84 on macrophages for cancer immunotherapy
  69. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data
  70. SUSD2 suppresses CD8+ T cell antitumor immunity by targeting IL-2 receptor signaling
  71. scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data
  72. MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data
  73. Single Cell RNA sequencing (scRNAseq) of fresh human lung cell suspension v1
  74. Androgen conspires with the CD8 + T cell exhaustion program and contributes to sex bias in cancer
  75. Microglia coordinate cellular interactions during spinal cord repair in mice
  76. Deep learning analysis of single‐cell data in empowering clinical implementation
  77. Biological aging of CNS-resident cells alters the clinical course and immunopathology of autoimmune demyelinating disease
  78. The use of single-cell multi-omics in immuno-oncology
  79. Author Correction: scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
  80. Treatment with soluble CD24 attenuates COVID-19-associated systemic immunopathology
  81. DESSO-DB: A web database for sequence and shape motif analyses and identification
  82. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
  83. Assessing deep learning methods in cis -regulatory motif finding based on genomic sequencing data
  84. Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data
  85. Prediction of protein–protein interactions based on elastic net and deep forest
  86. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
  87. scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder
  88. Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics
  89. DeepMal: Accurate prediction of protein malonylation sites by deep neural networks
  90. Single-Cell Techniques and Deep Learning in Predicting Drug Response
  91. Integrative Methods and Practical Challenges for Single-Cell Multi-omics
  92. Abstract 4409: Towards cell-type-specific gene regulation in heterogeneous cancer cells
  93. IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq
  94. WFhb1-1 plays an important role in resistance against Fusarium head blight in wheat
  95. DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion
  96. Network analyses in microbiome based on high-throughput multi-omics data
  97. Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks
  98. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting
  99. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome
  100. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data
  101. Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework
  102. Clustering and classification methods for single-cell RNA-sequencing data
  103. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome
  104. The Genetics and Genome-Wide Screening of Regrowth Loci, a Key Component of Perennialism in Zea diploperennis
  105. Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique
  106. A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks
  107. RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis
  108. It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data
  109. A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis