24. Zhang, Y., Harris, C.J., Liu, Q., Liu, W., Ausin, I., Long, Y., Xiao, L., Feng, L., Chen, X., Xie, Y., Chen, X., Zhan, L., Feng, S., Li, J.J., Wang, H., Zhai, J., and Jacobsen. S.E. (2018). Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis. Proc Natl Acad Sci. USA 115(5):E1069-E1074.
23. Jonassaint, C.R., Kang, C., Abrams, D.M., Li, J.J., Mao, J., Jia, Y., Long, Q., Sanger, M., Jonassaint, J.C., De Castro, L., and Shah, N. (2018). Understanding patterns and correlates of daily pain using the sickle cell disease mobile application to record symptoms via technology (SMART). British Journal of Haematology 183(2):306-308.
22. Li, J.J., Chew, G.-L., and Biggin, M.D. (2017). Quantitating translational control: mRNA abundancee-dependent and independent contributions and the mRNA sequences that specify them. Nucleic Acids Research 45(20):11821-11836. [ Highlight talk at RECOMB 2018 ]
21. Clifton, S.M., Kang, C., Li, J.J., Long, Q., Shah, N., and Abrams, D.M. (2017). Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain. Journal of Computational Biology 24(7):675-688.
20. Tong, X. and Li, J.J. (2017). Discussion of “Random-projection ensemble classification” by Cannings, T.I. and Samworth, R.J. Journal of the Royal Statistical Society: Series B 79(4):1025-1026.
19. Li, W.V., Chen, Y., and Li, J.J. (2017). TROM: a testing-based method for finding transcriptomic similarity of biological samples. Statistics in Biosciences 9(1):105-136. [ SOFTWARE ]
18. Gao, R. and Li, J.J. (2017). Correspondence of D. melanogaster and C. elegans developmental stages revealed by alternative splicing characteristics of conserved exons. BMC Genomics 18:234.
17. Yang, Y.*, Yang, Y.T.*, Yuan, J., Lu, Z.J., and Li, J.J. (2017). Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states. Nucleic Acids Research 45(4):1657-1672. [ DATA ]
16. Li, J.J. and Tong, X. (2016). Genomic applications of the Neyman-Pearson classification paradigm. Big Data Analytics in Genomics. Springer (New York).
15. Ye, Y. and Li, J.J. (2016). NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data. BMC Genomics 17(Supp 1):11. [ SOFTWARE ]