| TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data L Wei, Z Jin, S Yang, Y Xu, Y Zhu, Y Ji Bioinformatics 34 (9), 1615-1617, 2018 | 209 | 2018 |
| Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data W Guo, H Li, Y Zhu, L Lan, S Yang, K Drukker, E Morris, E Burnside, ... Journal of medical imaging 2 (4), 041007-041007, 2015 | 162 | 2015 |
| A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2 W Guo, SJ Wang, S Yang, H Lynn, Y Ji Contemporary clinical trials 58, 23-33, 2017 | 83 | 2017 |
| Imaging-genomic study of head and neck squamous cell carcinoma: associations between radiomic phenotypes and genomic mechanisms via integration of the cancer genome atlas and … Y Zhu, ASR Mohamed, SY Lai, S Yang, A Kanwar, L Wei, M Kamal, ... JCO clinical cancer informatics 3, 1-9, 2019 | 57 | 2019 |
| Zodiac: a comprehensive depiction of genetic interactions in cancer by integrating TCGA data Y Zhu, Y Xu, DL Helseth Jr, K Gulukota, S Yang, LL Pesce, R Mitra, ... Journal of the National Cancer Institute 107 (8), djv129, 2015 | 37 | 2015 |
| Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals S Yang, Y Liu, N Jiang, J Chen, L Leach, Z Luo, M Wang BMC genomics 15, 1-12, 2014 | 37 | 2014 |
| Polygenic molecular architecture underlying non-sexual cell aggregation in budding yeast J Li, L Wang, X Wu, O Fang, L Wang, C Lu, S Yang, X Hu, Z Luo DNA research 20 (1), 55-66, 2013 | 27 | 2013 |
| Why do essential proteins tend to be clustered in the yeast interactome network? C Lu, X Hu, G Wang, LJ Leach, S Yang, MJ Kearsey, ZW Luo Molecular Biosystems 6 (5), 871-877, 2010 | 20 | 2010 |
| Bayesian nonparametric clustering for large data sets DA Zuanetti, P Müller, Y Zhu, S Yang, Y Ji Statistics and Computing 29, 203-215, 2019 | 18 | 2019 |
| An integrated dose-finding tool for phase I trials in oncology S Yang, SJ Wang, Y Ji Contemporary clinical trials 45, 426-434, 2015 | 18 | 2015 |
| Clustering distributions with the marginalized nested Dirichlet process DA Zuanetti, P Müller, Y Zhu, S Yang, Y Ji Biometrics 74 (2), 584-594, 2018 | 15 | 2018 |
| On the interval-based dose-finding designs Y Ji, S Yang arXiv preprint arXiv:1706.03277, 2017 | 8 | 2017 |
| Court L, Ji Y, Fuller CD. 2019 Y Zhu, ASR Mohamed, SY Lai, S Yang, A Kanwar, L Wei, M Kamal, ... Imaging-genomic study of head and neck squamous cell carcinoma: associations …, 2019 | 6 | 2019 |
| Deep Learning–Based H-Score Quantification of Immunohistochemistry-Stained Images Z Wen, D Luo, S Wang, R Rong, BM Evers, L Jia, Y Fang, EV Daoud, ... Modern Pathology 37 (2), 100398, 2024 | 3 | 2024 |
| SAFARI: shape analysis for AI-segmented images E Fernández, S Yang, SH Chiou, C Moon, C Zhang, B Yao, G Xiao, Q Li BMC Medical Imaging 22 (1), 129, 2022 | 3 | 2022 |
| TU‐CD‐BRB‐06: Deciphering Genomic Underpinnings of Quantitative MRI‐Based Radiomic Phenotypes of Invasive Breast Carcinoma Y Zhu, H Li, W Guo, K Drukker, S Yang, L Lan, M Giger, Y Ji, ... Medical Physics 42 (6Part32), 3603-3603, 2015 | 3 | 2015 |
| Deep-learning-based hepatic ploidy quantification using H&E histopathology images Z Wen, YH Lin, S Wang, N Fujiwara, R Rong, KW Jin, DM Yang, B Yao, ... Genes 14 (4), 921, 2023 | 2 | 2023 |
| Tumor Evolution Decoder (TED): Unveiling Tumor Evolution Based on Mutation Profiles of Subclones or Single Cells Y Zhu, S Sengupta, L Wei, S Yang, Y Ji bioRxiv, 633610, 2019 | | 2019 |
| A next-generation statistical tool for designing phase I trials. Y Ji, S Yang Journal of Clinical Oncology 32 (15_suppl), e14022-e14022, 2014 | | 2014 |
| TCGA-Assembler 2: Software Pipeline for Retrieval and Processing of TCGA/CPTAC Data (Supplementary Information) L Wei, Z Jin, S Yang, Y Xu, Y Zhu, Y Ji | | |
