Recent publications

  1. Y. Zhu, P. Yan, R. Wang, J. Lai, H. Tang, X. Xiao, R. Yu…Y. Chen, K. Wang. (2023). Opioid-induced fragile-like regulatory T cells contribute to withdrawal, Cell, 186
  2. Lin, Y., Wu, S., Xiao, X., Zhao, J., Wang, M., Li, H., ... & Yu, R. (2022). Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNNXMBD. STAR protocols, 3(3), 101587.
  3. X. Xiao, Q. Guo, C. Cui, Y. Lin, L. Zhang, X. Ding, Q. Li, M. Wang, W. Yang, Y. Kong & R. Yu (2022), Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma. Communications medicine, 2, 131.
  4. Liu, Y., Lin, Y., Yang, W., Lin, Y., Wu, Y., Zhang, Z., ... & Yu, R. Application of individualized differential expression analysis in human cancer proteome. Briefings in Bioinformatics.
  5. Lin, Y., Li, H., Xiao, X., Zhang, L., Wang, K., Zhao, J., ... & Yu, R. (2022). DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks. Patterns, 100440.
  6. Wang, L., Zhou, L., Yang, W., & Yu, R. (2022). Deepfakes: a new threat to image fabrication in scientific publications?. Patterns, 3 (5), 100509.
  7. Liu, Y., Gao, M., Tan, L., Liu, H., Lin, Y., Yang, W., & Yu, R. (2021, December). scSpark XMBD: High-Performance scRNA-seq Data Processing with Spark. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  (pp. 1956-1962). IEEE.
  8. Gao, M., Yang, W., Li, C., Chang, Y., Liu, Y., He, Q., Zhong, C.Q., Shuai, J., Yu, R*. and Han, J.*, 2021. Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics. Communications biology, 4(1), pp.1-10.
  9. Xiao, X., Xu, C., Yang, W. and Yu, R., 2021. Inconsistent prediction capability of ImmuneCells. Sig across different RNA-seq datasets. Nature communications, 12(1), pp.1-3.
  10. Huang, L., Hong, B., Yang, W., Wang, L. and Yu, R., 2021. Snipe: highly sensitive pathogen detection from metagenomic sequencing data. Briefings in Bioinformatics, 22(5), p.bbab064.
  11. Li, C., Gao, M., Yang, W., Zhong, C. and Yu, R., 2021. Diamond: a multi-modal DIA mass spectrometry data processing pipeline. Bioinformatics, 37(2), pp.265-267.
  12. J. Guo, Y. Zhou, C. Xu, Q. Chen, Z. Sztupinszki, J. Börcsök, C. Xu, F. Ye, W. Tang, J. Kang, L. Yang, J. Zhong, T. Zhong, T. Hu, R. Yu, Z. Szallasi, X. Deng, Q. Li, 2021. Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers. Cancer Research, 81(16), pp.4205-4217.
  13. X. Xiao, Y. Qiao, Y. Jiao, N. Fu, W. Yang, L. Wang, R. Yu*, J. Han*，2021. Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry, Frontiers in Genetics, (12)
  14. Hou, W., Wang, L., Cai, S., Lin, Z., Yu, R. and Qin, J., 2021. Early neoplasia identification in Barrett’s esophagus via attentive hierarchical aggregation and self-distillation. Medical image analysis, 72, p.102092.
  15. Z. Wang, X. Fan*, J. Qi, C. Wang, R. Yu, C. Wen, 2021. Federated Learning with Fair Averaging, 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Main Track
  16. Yu, R., Yang, W. and Wang, S., 2020. Performance evaluation of lossy quality compression algorithms for RNA-seq data. BMC bioinformatics, 21(1), pp.1-15.
  17. Yu, R. and Yang, W., 2020. ScaleQC: a scalable lossy to lossless solution for NGS data compression. Bioinformatics, 36(17), pp.4551-4559.