He, Z., Chu, B., Yang, J., Gu, J., Chen, Z., Liu, L., Morrison, T., Belloy, M. E., Qi, X., Hejazi, N., Mathur, M., Le Guen, Y., Tang, H., Hastie, T., Ionita-Laza, I., Sabatti, C. and Candès, E. Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics. Submitted.
Liu, L. and Ma, L. Spatial properties of Bayesian unsupervised trees. Proceedings of Thirty Seventh Conference on Learning Theory (COLT), PMLR 247: 3556–3581, 2024.
Feng, H., Lu, X.J., Maji, S., Liu, L., Ustianenko, D., Rudnick, N. D. and Zhang, C. Structure-based prediction and characterization of photo-crosslinking in native protein-RNA complexes. Nature Communications, 15(1): 2279, 2024.
Yang, Z., Wang, C., Liu, L., Khan, A., Lee, A., Vardarajan, B., Mayeux, R., Kiryluk, K. and Ionita-Laza, I. CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses. Nature Genetics, 55(6): 1057–1065, 2023.
Liu, L., Li, D. and Wong, W. H. Convergence rates of a class of multivariate density estimation methods based on adaptive partitioning. Journal of Machine Learning Research, 24(50): 1–64, 2023.
Ma, S., Wang, C., Khan, A., Liu, L., Dalgleish, J., Kiryluk, K., He, Z. and Ionita-Laza, I. BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data. Genome Biology, 24(1): 24, 2023.
He, Z., Liu, L., Belloy, M. E., Le Guen, Y., Sossin, A., Liu, X., Qi, X., Ma, S., Wyss-Coray, T., Tang, H., Sabatti, C., Candès, E., Greicius, M. and Ionita-Laza, I. GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies. Nature Communications, 13(1): 7209, 2022.
Liu, L., Meng, Y., Wu, X., Ying, Z. and Zheng, T. Log-rank-type tests for equality of distributions in high-dimensional spaces. Journal of Computational and Graphical Statistics, 31(4): 1384–1396, 2022.
Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z. and Ionita-Laza, I. KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10): 1761–1776, 2022.
He, Z., Le Guen, Y., Liu, L., Lee, J., Ma, S., Yang, A. C., Liu, X., Rutledge, J., Losada, P. M., Song, B., Belloy, M. E., Butler, R. R., Longo, F. M., Tang, H., Mormino, E. C., Wyss-Coray, T., Greicius, M. and Ionita-Laza, I. Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics. The American Journal of Human Genetics, 108(12): 2336–2353, 2021.
Ma, S., Dalgleish, J., Lee, J., Wang, C., Liu, L., Gill, R., Buxbaum, J. D., Chung, W. K., Aschard, H., Silverman, E. K., Cho, M. H., He, Z. and Ionita-Laza, I. Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes. Proceedings of the National Academy of Sciences USA, 118(47): e2105191118, 2021.
He, Z., Liu, L., Wang, C., Le Guen, Y., Lee, J., Gogarten, S., Lu, F., Montgomery, S., Tang, H., Silverman, E., Cho, M., Greicius, M. and Ionita-Laza, I. Identification of putative causal loci in whole-genome sequencing data via knockoff statistics. Nature Communications, 12(1): 3152, 2021.
He, Z., Liu, L., Wang, K. and Ionita-Laza, I. A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs. Nature Communications, 9(1): 5199, 2018.
Liu, L., Li, D. and Wong, W. H. Convergence rates of a partition based Bayesian multivariate density estimation method. Advances in Neural Information Processing Systems (NIPS) 30: 4738–4746, 2017.
Ye, C., Liu, L., Wang, X. and Zhang, X. Observations on potential novel transcripts from RNA-Seq data. Frontiers of Electrical and Electronic Engineering in China, 6(2): 275–282, 2011.