All Published – Page 4 – The Junction of Statistics and Biology

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

73. Song, D., Wang, Q., Yan, G., Liu, T., and Li, J.J. (2024). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology 42:247-252. [ SOFTWARE ] [ PDF ]

Hierarchical Neyman-Pearson classification for prioritizing severe disease categories in COVID-19 patient data

74. Wang, L., Wang, Y.X.R., Li, J.J., and Tong, X. (2024). Hierarchical Neyman-Pearson classification for prioritizing severe disease categories in COVID-19 patient data. Journal of American Statistical Association 119:39-51.

A Python Package itca for Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multiclass Classification

72. Zhang, C., Zhang, S., and Li, J.J. (2023). A Python package itca for information-theoretic classification accuracy: a criterion that guides data-driven combination of ambiguous outcome labels in multiclass classification. Journal of Computational Biology 30(11):1246-1249. (RECOMB 2023; software article; see Publication 65 for the method article) [ SOFTWARE ]

scReadSim: a single-cell RNA-seq and ATAC-seq read simulator

71. Yan, G., Song, D., and Li, J.J. (2023). scReadSim: a single-cell RNA-seq and ATAC-seq read simulator. Nature Communications 14:7482. [ SOFTWARE ] [ PDF ]

Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

70. Xi, N.M. and Li, J.J. (2023). Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data. Computational and Structural Biotechnology Journal 21:4079-4095.

How the Monty Hall problem is similar to the false discovery rate in high-throughput data analysis

69. Li, J.J. (2023). How the Monty Hall problem is similar to the false discovery rate in high-throughput data analysis. Nature Biotechnology 41:754-755. | [ PDF ]

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 ]