All Published

Functional characterization of age-dependent p16 epimutation reveals biological drivers and therapeutic targets for colorectal cancer

68. Yang, L., Chen, X., Lee, C., Shi, J., Lawrence, E.B., Zhang, L., Li, Y., Gao, N., Jung, S.Y., Creighton, C.J., Li, J.J., Cui, Y., Arimura, S., Lei, Y., Li, W., Shen, L. (2023). Functional characterization of age-dependent p16 epimutation reveals biological drivers and therapeutic targets for colorectal cancer. Journal of Experimental & Clinical Cancer […]

METTL3-Mediated m6A Modification Controls Splicing Factor Abundance and Contributes to Aggressive CLL

67. Wu, Y., Jin, M., Fernandez, M., Hart, K.L., Liao, A., Ge, X., Fernandes, S.M., McDonald, T., Chen, Z., Röth, D., Ghoda, L.Y., Marcucci, G., Kalkum, M., Pillai, R.K., Danilov, A.V., Li, J.J., Chen, J., Brown, J.R., Rosen, S.T., Siddiqi, T., Wang, L. (2023). METTL3-mediated m6A modification controls splicing factor abundance and contributes to aggressive […]

Transcriptomic congruence analysis for evaluating model organisms.

66. Zong, W., Rahman, T., Zhu, L., Zeng, X., Zhang, Y., Zou, J., Liu, S., Ren, Z., Li, J.J., Sibille, E., Lee, A.V., Oesterreich, S., Ma, T., Tseng, G.C. (2023). Transcriptomic congruence analysis for evaluating model organisms. Proc Natl Acad Sci. USA 120(6):e2202584120.

Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification

65. Zhang, C., Chen, Y.E., Zhang, S., and Li, J.J. (2022). Information-theoretic classification accuracy: a criterion that guides data-driven combination of ambiguous outcome labels in multi-class classification. Journal of Machine Learning Research 23(341):1-65. [ RECOMB 2023 ] [ SOFTWARE ] [ PDF ]

PCA outperforms popular hidden variable inference methods for QTL mapping

64. Zhou, H.J., Li, L., Li, Y., Li, W., and Li, J.J. (2022). PCA outperforms popular hidden variable inference methods for QTL mapping. Genome Biology 23:210. [ Highlight talk at RECOMB 2023 ] [ SOFTWARE ] | [ PDF ]

Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation

63. Say, I., Chen, Y.E., Sun, M.Z., Li, J.J., and Lu, D.C. (2022). Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation. Frontiers in Rehabilitation Sciences 3:1005168.

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

62. Cui, E.H.*, Song, D.*, Wong, W.K., and Li, J.J. (2022). Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime. Bioinformatics 38(16):3927-3934. [ SOFTWARE ] [ CODE ]