| An adaptive association test for microbiome data C Wu, J Chen, J Kim, W Pan Genome medicine 8, 1-12, 2016 | 90 | 2016 |
| Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence KS Wang, G Yu, C Xu, XH Meng, J Zhou, C Zheng, Z Deng, L Shang, ... BMC medicine 19, 1-12, 2021 | 89 | 2021 |
| Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images G Yu, K Sun, C Xu, XH Shi, C Wu, T Xie, RQ Meng, XH Meng, KS Wang, ... Nature communications 12 (1), 6311, 2021 | 82 | 2021 |
| A review of integrative imputation for multi-omics datasets M Song, J Greenbaum, J Luttrell IV, W Zhou, C Wu, H Shen, P Gong, ... Frontiers in Genetics 11, 570255, 2020 | 79 | 2020 |
| A powerful framework for integrating eQTL and GWAS summary data Z Xu, C Wu, P Wei, W Pan Genetics 207 (3), 893-902, 2017 | 73 | 2017 |
| Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study M Bose, C Wu, JS Pankow, EW Demerath, J Bressler, M Fornage, ... BMC bioinformatics 15, 1-10, 2014 | 63 | 2014 |
| Imaging-wide association study: integrating imaging endophenotypes in GWAS Z Xu, C Wu, W Pan, Alzheimer's Disease Neuroimaging Initiative Neuroimage 159, 159-169, 2017 | 61 | 2017 |
| Asymptotically independent U-statistics in high-dimensional testing Y He, G Xu, C Wu, W Pan Annals of statistics 49 (1), 154, 2021 | 50 | 2021 |
| A new algorithm and theory for penalized regression-based clustering C Wu, S Kwon, X Shen, W Pan Journal of Machine Learning Research 17 (188), 1-25, 2016 | 42 | 2016 |
| An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk L Wu, Y Yang, X Guo, XO Shu, Q Cai, X Shu, B Li, R Tao, C Wu, JB Nikas, ... Nature communications 11 (1), 3905, 2020 | 38 | 2020 |
| Integration of enhancer-promoter interactions with GWAS summary results identifies novel schizophrenia-associated genes and pathways C Wu, W Pan Genetics 209 (3), 699-709, 2018 | 37 | 2018 |
| Partner selection in sustainable supply chains: A fuzzy ensemble learning model C Wu, C Lin, D Barnes, Y Zhang Journal of Cleaner Production 275, 123165, 2020 | 36 | 2020 |
| A transcriptome-wide association study of Alzheimer’s disease using prediction models of relevant tissues identifies novel candidate susceptibility genes Y Sun, J Zhu, D Zhou, S Canchi, C Wu, NJ Cox, RA Rissman, ... Genome medicine 13, 1-11, 2021 | 33 | 2021 |
| A transcriptome-wide association study identifies candidate susceptibility genes for pancreatic cancer risk D Liu, D Zhou, Y Sun, J Zhu, D Ghoneim, C Wu, Q Yao, ER Gamazon, ... Cancer research 80 (20), 4346-4354, 2020 | 32 | 2020 |
| A powerful fine-mapping method for transcriptome-wide association studies C Wu, W Pan Human genetics 139 (2), 199-213, 2020 | 31 | 2020 |
| An integrative multiomics analysis identifies putative causal genes for COVID-19 severity L Wu, J Zhu, D Liu, Y Sun, C Wu Genetics in Medicine 23 (11), 2076-2086, 2021 | 29 | 2021 |
| Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia C Wu, W Pan Genetic epidemiology 42 (3), 303-316, 2018 | 26 | 2018 |
| Associations between genetically predicted blood protein biomarkers and pancreatic cancer risk J Zhu, X Shu, X Guo, D Liu, J Bao, RL Milne, GG Giles, C Wu, M Du, ... Cancer Epidemiology, Biomarkers & Prevention 29 (7), 1501-1508, 2020 | 20 | 2020 |
| Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank C Wu Genetics 215 (4), 947-958, 2020 | 19 | 2020 |
| Integration of methylation QTL and enhancer–target gene maps with schizophrenia GWAS summary results identifies novel genes C Wu, W Pan Bioinformatics 35 (19), 3576-3583, 2019 | 19 | 2019 |
Wei PanUniversity of MinnesotaVerified email at umn.edu
