arXiv subjects

Xinran Qi

Publications and source records attributed to Xinran Qi.

3 recordsLinked to original sources

arXiv2024

Nonparametric Bayesian Knockoff Generators for Feature Selection Under Complex Data Structure

The recent proliferation of high-dimensional data, such as electronic health records and genetics data, offers new opportunities to find novel predictors of outcomes. Presented with a large set of candidate features, interest often lies in selecting the ones most likely to be predictive of an outcome for further study. Controlling the false discovery rate (FDR) at a specified level is often desired in evaluating these variables. Knockoff filtering is an innovative strategy for conducting FDR-controlled feature selection. This paper proposes a nonparametric Bayesian model for generating high-quality knockoff copies that can improve the accuracy of predictive feature identification for variables arising from complex distributions, which can be skewed, highly dispersed and/or a mixture of distributions. This paper provides a detailed description for generating knockoff copies from a GDPM model via MCMC posterior sampling. Additionally, we provide a theoretical guarantee on the robustness of the knockoff procedure. Through simulations, the method is shown to identify important features with accurate FDR control and improved power over the popular second-order Gaussian knockoff generator. Furthermore, the model is compared with finite Gaussian mixture knockoff generator in FDR and power. The proposed technique is applied for detecting genes predictive of survival in ovarian cancer patients using data from The Cancer Genome Atlas (TCGA).

stat.ME↗

arXiv2023

Designing ship hull forms using generative adversarial networks

We proposed a GAN-based method to generate a ship hull form. Unlike mathematical hull forms that require geometrical parameters to generate ship hull forms, the proposed method requires desirable ship performance parameters, i.e., the drag coefficient and tonnage. The requirements of ship owners are generally focused on the ship performance and not the geometry itself. Hence, the proposed model is useful for obtaining the ship hull form based on an owner's requirements. The GAN model was trained using a ship hull form dataset generated using the generalized Wigley hull form. The proposed method was evaluated through numerical experiments and successfully generated ship data with small errors.

cs.CE↗

arXiv2023

Robust inference with GhostKnockoffs in genome-wide association studies

Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex diseases. Motivated by GWASs' limitations in identifying small effect loci to understand complex traits' polygenicity and fine-mapping putative causal variants from proxy ones, we propose a knockoff-based method which only requires summary statistics from GWASs and demonstrate its validity in the presence of relatedness. We show that GhostKnockoffs inference is robust to its input Z-scores as long as they are from valid marginal association tests and their correlations are consistent with the correlations among the corresponding genetic variants. The property generalizes GhostKnockoffs to other GWASs settings, such as the meta-analysis of multiple overlapping studies and studies based on association test statistics deviated from score tests. We demonstrate GhostKnockoffs' performance using empirical simulation and a meta-analysis of nine European ancestral genome-wide association studies and whole exome/genome sequencing studies. Both results demonstrate that GhostKnockoffs identify more putative causal variants with weak genotype-phenotype associations that are missed by conventional GWASs.

stat.ME↗