Note: [J] for journals, [P] for preprints, [C] for conference proceedings.

Statistical Genetics and Genomics

  • [1] Tang Y, Cabreros I, Storey JD. (2026) Identifying causal genotype-phenotype relationships for population-sampled parent-child trios [J]. Genetic Epidemiology 50(1): e70027. DOI:10.1002/gepi.70027

  • [2] Tang Y and Storey JD. (2025) A generalized test of genotype-phenotype causality in population-sampled nuclear families [P]. bioRxiv. DOI:10.64898/2025.12.29.696865

  • [3] Tang Y, Cabreros I, Storey JD. (2024) Identifying causal genotype-phenotype relationships for population-sampled parent-child trios [P]. bioRxiv. DOI:10.1101/2024.12.10.627752

  • [4] Tang Y and Storey JD. (2022) On the polygenic trait model in population-based human genetics studies: What is random and what is fixed [C]. The 72nd Annual Meeting of The American Society of Human Genetics. 2022 Sep. Reviewer’s Choice Abstract Award (PDF)

Biomedical Data

  • [5] Zhao M, Tang Y, Kim H, and Hasegawa K. (2018) Machine learning with k-means dimensional reduction for predicting survival outcomes in patients with breast cancer [J]. Cancer Informatics 17: 1176935118810215. DOI:10.1177/1176935118810215

Environmental Statistics

  • [6] Xiong F, Su Z, Tang Y, Dai T, Wen D. (2024) Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence [J]. Environmental Science and Ecotechnology 20: 100370. DOI:10.1016/j.ese.2023.100370

  • [7] Li F, Bao Y, Chen L, Su Z, Tang Y, and Wen D. (2023) Screening of priority antibiotics in Chinese seawater based on the persistence, bioaccumulation, toxicity and resistance [J]. Environment International 179: 108140. DOI:10.1016/j.envint.2023.108140

  • [8] Su Z, Wen D, Gu AZ, Zheng Y, Tang Y, and Chen L. (2023) Industrial effluents boosted antibiotic resistome risk in coastal environments [J]. Environment International 171: 107714. DOI:10.1016/j.envint.2022.107714

  • [9] Tang Y, Dai T, Su Z, Hasegawa K, Tian J, Chen L, and Wen D. (2020) A tripartite microbial-environment network indicates how crucial microbes influence the microbial community ecology [J]. Microbial Ecology 79: 342–356. DOI:10.1007/s00248-019-01421-8

  • [10] Dai T, Zhang Y, Ning D, Su Z, Tang Y, Huang B, Mu Q, and Wen D. (2018) Dynamics of sediment microbial functional capacity and community interaction networks in an urbanized coastal estuary [J]. Frontiers in Microbiology 9: 2731. DOI:10.3389/fmicb.2018.02731

  • [11] Su Z, Dai T, Tang Y, Tao Y, Huang B, Mu Q, and Wen D. (2018) Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area [J]. Marine Pollution Bulletin 131: 481–495. DOI:10.1016/J.MARPOLBUL.2018.04.052

  • [12] Dai T, Zhang Y, Tang Y, Bai Y, Tao, Y, Huang B, and Wen D. (2016) Identifying the key taxonomic categories that characterize microbial community diversity using full-scale classification: a case study of microbial community in the sediments of Hangzhou Bay [J]. FEMS Microbiology Ecology 92(10): fiw150. DOI:10.1093/femsec/fiw150