Qiuran Lyu Homepage

Finetuning LLM for Alzheimer’s Disease Diagnosis and Progression Prediction — We investigated different serialized ways (e.g. Markdown, plain text, feature-wise, and visit-wise) for longitudinal tabular data from ADNI and HABS-HD as LLM inputs and finetuned Llama 3 and Llama 3.1 tailored to Alzheimer’s disease outcomes prediction. We are working on developing a statistical metric to construct an alpha-level confidence set to characterize the variable importance under the LLM context.

Mediation Analysis with Mendelian Randomization and Efficient Multiple GWAS Integration — We used structural equations to construct the relationship between the mediator, exposure, and outcome effect based on the causal diagram. A three-step procedure was designed for conducting mediation analysis with integrated multiple GWAS using joint rerandomization and Rao-blackwellization to eliminate the measurement error bias, the winner's curse, the loser's curse, and the imperfect IV selection issue. See preprint, links to code and package .

On the Theoretical Investigation of Mediation Analysis with Mendelian Randomization and Summary Data — We provide rigorous statistical analysis of existing two popular frameworks for conducting mediation analysis with Mendelian Randomization. See preprint .

Benchmark of different QTL pipelines (including isoform-QTL, eQTL, and splicing-QTL) — We compared the performance of RSEM, Kallisto, Cufflinks, Salmon + FastQTL, eQTL, and Leafcutter on the simulated dataset. We empirically demonstrated that isoform-QTL pipelines outperform all others. Among all isoform-QTL pipelines, Cufflinks has the best performance in terms of power and false discovery rate. See slides (preparing Manuscript).

GMS training framework and WMMLP — We constructed the weighted multiplicative MLP (WMMLP) in PyTorch based on Taylor expansion of M estimators and used neural networks to solve the M-estimation problem under the bootstrap and cross-validation context. See final summer research report.

Research