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.
Selected publications
- Copy number analysis and inference of subclonal populations in cancer genomes using Sclust.Yupeng Cun*, Tsun-Po Yang*, Viktor Achter*, Ulrich Lang, Martin Peifer, Nature Protocols, (2018) [Clonal evolution, Statistical modelling]
- Network and data integration for biomarker signature discovery via network smoothed T-statistics. Yupeng Cun, Holger Fröhlich, PLoS ONE，2013; 8(9):e73074., DOI: 10.1371/journal.pone.0073074 [Network Biology, Statistical modelling]
- MIF-expressing tumor cells mediate immunotherapeutic resistance in esophageal squamous cell carcinoma. Jing Song, Xiaomei Song, Yue Xie, Hong Guo*, Yupeng Cun*. Theranostics 2026; 16(3):1613-1629. doi:10.7150/thno.118269. [Tumor treatment, Data mining]
- Multi-omics analysis reveals the evolutionary origin of diterpenoid alkaloid biosynthesis pathways in Aconitum. Dake Zhao+, Ya Zhang+, Huanxing Ren, …, Jue Ruan, Suiyun Chen*, Diqiu Yu*, Yupeng Cun*, Journal of Integrative Plant Biology 2023, DOI: 10.1111/jipb.13565 (中文介绍) [Plant genome de no assemble, Network biology, Multi-omoic Integration]
- Integrating multi-omics data reveals neuroblastoma subtypes in the tumor microenvironment, Jinhua Fan, Shuxin Tang, Xiangru Kong, Yupeng Cun*, Life Sciences, 2024, (359), 123236. doi: 10.1016/j.lfs.2024.123236 [Multi-omoic Integration, Network biology]
- A comprehensive comparison on current deep learning approaches for plant images classification, Chengli Zhou, Linmei Ge, Yanbu Guo, Dongming Zhou*, Yupeng Cun*. (2021) J. Phys.: Conf. Ser. 1873 012002 (IWECAI 2021) (EI, conference paper). [Deep learning, Plant image classification]
我们的研究重点是发展计算生物学中的机器学习、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：
- >计算基因组学
- 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
计算基因组学研究组 