Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways. - Jian Zhang 084; Yan Wang 023; Desi Shang; Fulong Yu; Wei Liu 187; Yan Zhang; Chenchen Feng; Qiuyu Wang; Yanjun Xu; Yuejuan Liu; Xuefeng Bai 002; Xuecang Li; et al. (J. Biomed. Informatics, 2015)

A comprehensive overview of lncRNA annotation resources. - Jinyuan Xu; Jing Bai 014; Xinxin Zhang; Yanling Lv; Yonghui Gong; Ling Liu; Hongying Zhao; Fulong Yu; Yanyan Ping; Guanxiong Zhang; Yujia Lan; Yun Xiao 001; et al. (Briefings Bioinform., 2017)

LnChrom: a resource of experimentally validated lncRNA-chromatin interactions in human and mouse. - Fulong Yu; Guanxiong Zhang; Aiai Shi; Jing Hu; Feng Li; Xinxin Zhang; Yan Zhang; Jian Huang 004; Yun Xiao 001; Xia Li 004; Shujun Cheng (Database J. Biol. Databases Curation, 2018)

SEECancer: a resource for somatic events in evolution of cancer genome. - Hongyi Zhang; Shangyi Luo; Xinxin Zhang; Jianlong Liao; Fei Quan; Erjie Zhao; Chenfen Zhou; Fulong Yu; Wenkang Yin; Yunpeng Zhang; Yun Xiao 001; Xia Li 004 (Nucleic Acids Res., 2018)

Breast cancer prognosis signature: linking risk stratification to disease subtypes. - Fulong Yu; Fei Quan; Jinyuan Xu; Yan Zhang; Yi Xie; Jingyu Zhang; Yujia Lan; Huating Yuan; Hongyi Zhang; Shujun Cheng; Yun Xiao 001; Xia Li 004 (Briefings Bioinform., 2019)

scTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures. - Yan Zhang; Yaru Zhang; Jun Hu 010; Ji Zhang; Fangjie Guo; Meng Zhou 003; Guijun Zhang; Fulong Yu; Jianzhong Su (Bioinform., 2020)

CFEA: a cell-free epigenome atlas in human diseases. - Fulong Yu; Kai Li; Shuangquan Li; Jiaqi Liu; Yan Zhang; Meng Zhou 003; Hengqiang Zhao; Hongyan Chen; Nan Wu; Zhihua Liu; Jianzhong Su (Nucleic Acids Res., 2020)

CUT&RUNTools 2.0: a pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis. - Fulong Yu; Vijay G. Sankaran; Guo-Cheng Yuan (Bioinform., 2021)

Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models. - Yuxi Lin; Yaxue Fang; Zehong Zhang; Zhouwu Liu; Siyun Zhong; Fulong Yu (CoRR, 2025)