All Stories

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