Qiao Liu, PhD
Assistant Professor of BiostatisticsAbout
Research
Overview
- Multiomics Integration: We develop statistical and AI-driven methods to integrate diverse molecular data modalities, including genetics, transcriptomics, epigenomics, radiomics at different scale and resolution. Our goal is to build scalable and interpretable computational frameworks that can jointly model heterogeneous multiomics data, recover missing modalities, quantify uncertainty, and reveal regulatory programs across molecular layers.
- Causal Inference: We develop causal inference methodologies for high-dimensional biomedical data. By combining modern AI techniques and rigorious statistical inference (e.g., Bayesian inference), we aim to 1) estimate causal effects, especially under high-dimensional covaraites or even hidden confounding variable, 2) identify disease-relevant drivers (gene, regualtory elements, regulatory programs) and support interpretable biological discovery.
- Single Cell Genomics: We develop computational tools for single cell analysis that capture cell hetergeneity, cell state transition, cell-cell/environment communications/response etc. Current reserach interests lie on identifying/discovering biological mechanisms to analyze time‑course data, lineage tracing, CRISPR and small‑molecule perturbation screens. Our models provide insights into gene regulation mechanisms by modeling cell development, transition, response, aging, etc.
- Pharmacogenomics: We study how genetic and molecular variation influences drug response, treatment sensitivity, and disease progression. By integrating genomics, transcriptomics, and clinical or experimental drug-response data, we aim to develop predictive and causal models that can help identify therapeutic targets and support precision medicine.
- Genomic Foundation Models: We build context-aware foundation models for regulatory genomics by leveraging large-scale sequence, epigenomic, transcriptomic, and perturbation datasets. Our goal is to develop context-aware models that can learn generalizable representations of gene regulation, predict molecular phenotypes across cell types and conditions, and enable downstream tasks such as genetic variant effect quantification, regulatory element discovery, and phenotype prediction.
Medical Research Interests
Artificial Intelligence; Causality; Computational Biology; Data Science; Deep Learning; Epigenomics; Gene Expression Regulation; Generative Artificial Intelligence; Genomics; Machine Learning; Single-Cell Analysis
Public Health Interests
Bioinformatics; Genetics, Genomics, Epigenetics; Bayesian Statistics; Modeling; Aging
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