1.
Klekota J, Brauner E, Schreiber SL. Identifying biologically active compound classes using phenotypic screening data and sampling statistics. J Chem Inf Model. 2005;45(6):1824-36. doi:10.1021/ci050087d.
1.
Schäfer M, Klein H-U, Schwender H. Integrative analysis of multiple genomic variables using a hierarchical Bayesian model. Bioinformatics. 2017;33(20):3220-3227. doi:10.1093/bioinformatics/btx356.
1.
McPherson AW, Roth A, Ha G, et al. ReMixT: clone-specific genomic structure estimation in cancer. Genome Biol. 2017;18(1):140. doi:10.1186/s13059-017-1267-2.
1.
Xiong Y, Soumillon M, Wu J, et al. A Comparison of mRNA Sequencing with Random Primed and 3’-Directed Libraries. Sci Rep. 2017;7(1):14626. doi:10.1038/s41598-017-14892-x.
1.
Zhang P, He D, Xu Y, et al. Genome-wide identification and differential analysis of translational initiation. Nat Commun. 2017;8(1):1749. doi:10.1038/s41467-017-01981-8.
1.
Zhu Z, Anttila V, Smoller JW, Lee PH. Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies. PLoS One. 2018;13(3):e0193256. doi:10.1371/journal.pone.0193256.
1.
Maier RM, Zhu Z, Lee SH, et al. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat Commun. 2018;9(1):989. doi:10.1038/s41467-017-02769-6.
1.
McFarland JM, Paolella BR, Warren A, et al. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nat Commun. 2020;11(1):4296. doi:10.1038/s41467-020-17440-w.
1.
Ma Y, De Jager PL. Designing an epigenomic study. Mult Scler. 2018;24(5):604-609. doi:10.1177/1352458517750770.
1.
Ding J, Condon A, Shah SP. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun. 2018;9(1):2002. doi:10.1038/s41467-018-04368-5.