科研动态
  • Clustering single-cell RNA-seq data with a model-based deep learning approach

    Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing ‘false’ zero count observations.

  • MATHLA: a robust framework for HLA‑peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism

    Background: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy.
    Results: We present a pan-allele HLA-peptide binding prediction framework-MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism.
    Conclusion: Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

  • Liquid Biopsy Applications in the Clinic

    The global liquid biopsy industry is expected to exceed $US5 billion by 2023. One application of liquid biopsy technology is the diagnosis of disease using biomarkers found in blood, urine, stool, saliva, and other biological samples from patients. These biomarkers could be DNA, RNA, protein, or even a cell. More recently, the use of cell-free DNA from plasma is emerging as an important minimally invasive tool for clinical diagnosis. The development of technology has increased the diversity of its application. Here, we discuss how liquid biopsies have been used in the clinic, and how personalized medicine are likely to use liquid biopsies in the near future.

  • A Comprehensive Survey of Immune Cytolytic Activity-Associated Gene Co-Expression Networks across 17 Tumor and Normal Tissue Types

    Cytolytic immune activity in solid tissue can be quantified by transcript levels of two genes, GZMA and PRF1, which is named the CYT score. A previous study has investigated the molecular and genetic properties of tumors associated CYT, but a systematic exploration of how co-expression networks across different tumors are shaped by anti-tumor immunity is lacking. Here, we examined the connectivity and biological themes of CYT-associated modules in gene co-expression networks of 14 tumor and 3 matched normal tissues constructed from the RNA-Seq data of the "The Cancer Genome Atlas" project.

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