生物信息学中心

黎斌 博士

生物信息学中心主任

Bin Li, Ph.D.

Director of Bioinformatics Center

Email: libin@nibs.ac.cn

Home page：www.nibslilab.com

个人简介

黎斌博士现为北京生命科学研究所生物信息学中心主任，致力于开发新型机器学习和深度学习算法助力精准医疗，深入分析基因组大数据，研究细胞发育和病变的生物学内在因素，探索流行性传染病和恶性肿瘤的发病机制和潜在的药物靶点，实现针对不同患者的个性化基因诊疗方案。研究成果先后发表在Nature Methods, Genome Biology, Nature Communications, Briefings in Bioinformatics等期刊上。

Dr. Li Bin is currently the director of the Bioinformatics Center of NIBS. He is committed to developing new machine learning and apply them to precision medicine, analyze genomic big data, study the biological factors of cell development and pathology, and explore the pathogenesis and potential drug targets of epidemic infectious diseases and malignant tumors. These research results have been published in Nature Methods, Genome Biology, Nature Communications, Briefings in Bioinformatics and other journals.

中心职能

生物信息学中心 Bioinformatics Center

近年来多种测序技术被广泛应用于基础生物学和临床医学研究，使我们可以从基因组层面理解细胞发育、分化、转化、病变的内在机制。然而，深入分析海量测序数据仍是难题，尤其是单细胞测序数据规模的指数级提高，亟需高效准确的生物信息学算法和工具。NIBS生物信息学中心为各个实验室提供生物信息学分析服务，包括对单细胞基因组、空间转录组、表观遗传组、蛋白质组等多种测序数据的深入解析和挖掘。同时，我们关注于开发新型机器学习和深度学习算法，结合大语言模型分析多种类型的大规模测序数据，探索各种重大疾病的发病机制，挖掘潜在的药物靶点，预测用药后的基因组变化，为患者的个性化精准诊疗提供支持。
In recent years, various sequencing technologies have been widely applied in basic biology and clinical medical research, allowing us to understand the intrinsic mechanisms of cell development, differentiation, transformation, and pathological changes at the genomic level. However, in-depth analysis of vast sequencing data remains a challenge, especially with the exponential increase in the scale of single-cell sequencing data, which urgently requires efficient and accurate bioinformatics algorithms and tools. The NIBS Bioinformatics Center provides bioinformatics analysis services for various laboratories, including in-depth interpretation and exploration of sequencing data from single-cell genomics, spatial transcriptomics, epigenomics, proteomics, and more. We also focus on developing novel machine learning and deep learning algorithms, integrating large language models to analyze various types of large-scale sequencing data, exploring the pathogenesis of diseases, identifying potential drug targets, predicting genomic changes after drug administration, and supporting personalized precision diagnosis and treatment for patients.

教育经历 Education

2003～2010         中国科学院大连化学物理研究所，理学博士

Ph.D., Dalian Institute of Chemical Physics, Chinese Academy of Sciences

1999～2003         中国科学技术大学，理学学士

Bachelor of science, University of science and technology of China

工作经历 Professional Experience

2021-至今      北京生命科学研究所                         生物信息学中心主任

Director of Bioinformatics Center, National Institute of Biological Science, Beijing

2016～2021    中国科学技术大学，生命科学与医学部           副研究员

Associate researcher, Department of life sciences and medicine, University of science and technology of China

2014～2016    美国哥伦比亚大学                                   博士后研究员

Postdoctoral researcher, Department of chemistry, Columbia University

2010～2014   美国加州大学伯克利分校                         博士后

Post-doctoral, Department of chemistry, University of California, Berkeley

代表性论文

1. Yinlei Hu^#, Siyuan Wan^#, Yuanhanyu Luo^#, Yuanzhe Li, Tong Wu, Wentao Deng, Chen Jiang, Shan Jiang, Yueping Zhang, Nianping Liu, Zongcheng Yang, Falai Chen*, Bin Li* & Kun Qu*, “Benchmarking algorithms for single-cell multi-omics prediction and integration”, Nature Methods, 21, 2181-2194 (2024).

2. Hao Xu, Shuyan Wang, Minghao Fang, Songwen Luo, Chunpeng Chen, Siyuan Wan, Rirui Wang, Meifang Tang, Tian Xue, Bin Li*, Jun Lin* & Kun Qu*, “SPACEL: deep learning-based characterization of spatial transcriptome architectures”, Nature Communications, 14, 7603 (2023).

3. Bin Li^#, Wen Zhang^#, Chuang Guo^#, Hao Xu, Longfei Li, Minghao Fang, Yinlei Hu, Xinye Zhang, Xinfeng Yao, Meifang Tang, Ke Liu, Xuetong Zhao, Jun Lin, Linzhao Cheng, Falai Chen, Tian Xue & Kun Qu, “Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution”, Nature Methods, 19, 662–670 (2022).

4. Bin Li^#, Young Li^#, Kun Li, Lianbang Zhu, Qiaoni Yu, Jingwen Fang, Pengfei Cai, Chen Jiang, Kun Qu, “APEC: An accesson-based method for single-cell chromatin accessibility analysis”, Genome Biology, 21, 116 (2020).

5. Chuang Guo^#, Bin Li^#, Huan Ma, Xiaofang Wang, Lianxin Liu, Xiaoling Ma, Jianping Weng, Haiming Wei, Tengchuan Jin*, Jun Lin*, Kun Qu*, “Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm”, Nature Communications, 11, 3924 (2020).