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Selected papers are below. For a full list of papers, please see Google Scholar.

Machine learning (ML)-assisted statistical inference

Machine learning is making predictions with uncertainty. How to make statistically rigorous scientific discovery with these "black-box" predictions?

- Valid Inference for Machine Learning-Assisted GWAS.
Miao J., Wu Y., Sun Z., Miao X., Lu T., Zhao J., Lu Q. (2024).
[Preprint] [Software] [Summary]

- Assumption-Lean and Data-Adaptive Post-Prediction Inference.
Miao J.*, Miao X.*, Wu Y., Zhao J., Lu Q. (2023).
[Preprint] [Software]

Heterogeneous treatment effect, gene-environment interactions, and genetic modifier

- Reimagining Gene-Environment Interaction Analysis for Human Complex Traits
Miao J., Song G., Wu Y., Hu J., Wu Y., Basu S., Andrews J., Schaumberg K., Fletcher J., Schmitz L., Lu Q. (2022).
[Preprint] [Software: PIGEON] [Summary]

- A Quantile Integral Linear Model to Quantify Genetic Effects on Phenotypic Variability.
Miao J., Lin Y., Wu Y., Zheng B., Schmitz L., Fletcher J., Lu Q. (2022).
[Journal] [Preprint] [Software: QUAIL] [Summary]

Generalizability and fairness of genetically-informed risk prediction across diverse contexts

- Quantifying Portable Genetic Effects and Improving Cross-Ancestry Genetic Prediction with GWAS Summary Statistics.
Miao J.*, Guo H.*, Song G., Zhao Z., Hou L., Lu Q. (2023).
Nature Communications
[Journal] [Preprint] [Software: X-Wing] [Summary]