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Showing 1–4 of 4 results
Advanced filters: Author: Xiongbin Kang Clear advanced filters

  • Disease phenotypes can be predicted from genetic profiles, but diseases with complex, non-additive interactions between genes are hard to disentangle. An approach called DiseaseCapsule makes use of capsule networks to identify the hierarchical structure in genomic data and can predict complex diseases such as amyotrophic lateral sclerosis with high accuracy.

    • Xiao Luo
    • Xiongbin Kang
    • Alexander Schönhuth
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 5, P: 114-125

  • Spermine is a polyamine dysregulated in different cancers. This group shows that Serpinb9 prevents spermine synthase degradation, raising spermine levels and impairing the tumor immune microenvironment, and identifying Serpinb9 as a potential therapeutic target for pancreatic cancer immunotherapy.

    • Hanshen Yang
    • Xiaozhen Zhang
    • Xueli Bai
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-19

  • Reconstructing the genomes of microbial communities at the level of their strains poses significant challenges because sequencing errors can obscure strain-specific variants. Here the authors present a strain-aware approach that utilizes the complementary strengths of next-generation sequencing (NGS) and third-generation sequencing (TGS) reads.

    • Xiongbin Kang
    • Wenhai Zhang
    • Alexander Schönhuth
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-17

  • Consensus sequence-based methods for self-correction of long-read sequencing data are affected by biases that can mask true variants characterizing little-covered or low-frequency haplotypes. Here, to address this issue, the authors develop a variation graph-based method for performing haplotype-aware self-correction of long reads.

    • Xiao Luo
    • Xiongbin Kang
    • Alexander Schönhuth
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12