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
Computational Genomics & Medicine 