Research

Our major research interest concerns the development of statistical and machine learning methods and softwares for computational biology. I am particularly interested in complex biological data modeling, such as high-throughput genomic data, network biology, population genomics and related evolutionary problems.

Currently, my research projects are mainly focus on following topics:

  • Computational genomics
    • Genetic variation calling methods in genomic data
    • Personality analysis in single cell omic data
    • Multi-omics modeling and analysis in disease(cancer)
    • Evolutionary genomics
  • Medical informatics
    • Applying deep learning to clinal image classification
    • Data ming in electronic health records data

我们的研究重点是发展计算生物学中的新的机器学习、数学模型和分析软件。 我们现在主要关注点是疾病(癌症)和罕见病在遗传变异和单细胞水平上变化,并找出与疾病表型相关的变异和单细胞微环境,希望探索疾病发生、发展的微观进化的“生活史”。

当前,我们与临床科研人员建立了密切地合作关系,在以下研究方向开展我们工作:

  • 进化基因组学
    • 单细胞组学数据的个性化算法开发和分析
    • 疾病癌症)的多组学数据数据建模和分析
    • 进化基因组学
  • 医学信息学
    • 深度学习在医学医学影像组学中的应用
    • 医学大数据中的挖掘

Softwares

  • COSINE: a web server for online subclonal inferring in cancer genomic data
  • Sclust:  A C++/R package for inference of subclonal populations in cancer genomes using smoothing splines. (If your download locate in China, the download speed of this site is much more faster: rj.run/downloads/Sclust.tgz for china mainland )
  • FrSVM:  an R program, short of Filter by highly ranked gene for Support Vector Machine, for microarray classification.
  • netClass: An R package for network-Based microarray Classification