| Deep learning–based multi-omics integration robustly predicts survival in liver cancer K Chaudhary, OB Poirion, L Lu, LX Garmire Clinical Cancer Research 24 (6), 1248-1259, 2018 | 831 | 2018 |
| Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation N Gagliani, MCA Vesely, A Iseppon, L Brockmann, H Xu, NW Palm, ... Nature 523 (7559), 221-225, 2015 | 822 | 2015 |
| More is better: recent progress in multi-omics data integration methods S Huang, K Chaudhary, LX Garmire Frontiers in genetics 8, 268903, 2017 | 642 | 2017 |
| Regulated accumulation of desmosterol integrates macrophage lipid metabolism and inflammatory responses NJ Spann, LX Garmire, JG McDonald, DS Myers, SB Milne, N Shibata, ... Cell 151 (1), 138-152, 2012 | 603 | 2012 |
| Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data T Ching, X Zhu, LX Garmire PLoS computational biology 14 (4), e1006076, 2018 | 358 | 2018 |
| Power analysis and sample size estimation for RNA-Seq differential expression T Ching, S Huang, LX Garmire Rna 20 (11), 1684-1696, 2014 | 261 | 2014 |
| DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data C Arisdakessian, O Poirion, B Yunits, X Zhu, LX Garmire Genome biology 20, 1-14, 2019 | 236 | 2019 |
| Enter the matrix: factorization uncovers knowledge from omics GL Stein-O’Brien, R Arora, AC Culhane, AV Favorov, LX Garmire, ... Trends in Genetics 34 (10), 790-805, 2018 | 214 | 2018 |
| Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data FM Alakwaa, K Chaudhary, LX Garmire Journal of proteome research 17 (1), 337-347, 2018 | 214 | 2018 |
| Targeted, high-depth, next-generation sequencing of cancer genes in formalin-fixed, paraffin-embedded and fine-needle aspiration tumor specimens AG Hadd, J Houghton, A Choudhary, S Sah, L Chen, AC Marko, ... The Journal of Molecular Diagnostics 15 (2), 234-247, 2013 | 204 | 2013 |
| Role of microRNA-23b in flow-regulation of Rb phosphorylation and endothelial cell growth KC Wang, LX Garmire, A Young, P Nguyen, A Trinh, S Subramaniam, ... Proceedings of the National Academy of Sciences 107 (7), 3234-3239, 2010 | 198 | 2010 |
| Subtypes of preeclampsia: recognition and determining clinical usefulness JM Roberts, JW Rich-Edwards, TF McElrath, L Garmire, L Myatt, ... Hypertension 77 (5), 1430-1441, 2021 | 161 | 2021 |
| Evaluation of normalization methods in mammalian microRNA-Seq data LX Garmire, S Subramaniam Rna 18 (6), 1279-1288, 2012 | 145 | 2012 |
| Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis S Huang, N Chong, NE Lewis, W Jia, G Xie, LX Garmire Genome medicine 8, 1-14, 2016 | 136 | 2016 |
| Single-cell transcriptomics bioinformatics and computational challenges OB Poirion, X Zhu, T Ching, L Garmire Frontiers in genetics 7, 207977, 2016 | 127 | 2016 |
| DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data OB Poirion, Z Jing, K Chaudhary, S Huang, LX Garmire Genome medicine 13, 1-15, 2021 | 112 | 2021 |
| Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma B Chen, L Garmire, DF Calvisi, MS Chua, RK Kelley, X Chen Nature Reviews Gastroenterology & Hepatology 17 (4), 238-251, 2020 | 111 | 2020 |
| Atheroprotective mechanisms of shear stress-regulated microRNAs RA Boon, E Hergenreider, S Dimmeler Thrombosis and haemostasis 108 (10), 616-620, 2012 | 99 | 2012 |
| Alternative splicing promotes tumour aggressiveness and drug resistance in African American prostate cancer BD Wang, K Ceniccola, SJ Hwang, R Andrawis, A Horvath, JA Freedman, ... Nature communications 8 (1), 15921, 2017 | 94 | 2017 |
| Evaluation of cell type annotation R packages on single-cell RNA-seq data Q Huang, Y Liu, Y Du, LX Garmire Genomics, Proteomics and Bioinformatics 19 (2), 267-281, 2021 | 87 | 2021 |
Xun ZhuSt. Jude Children's Research HospitalVerified email at stjude.org
