Public Health and Health Professions
Lou Lab
Led by Xiangyang Lou · Biostatistics
What we study
The Lou Lab develops statistical and computational methods to analyze biological data, focusing on integrating fragmented data and detecting interactions. Their work includes machine learning and hidden Markov models for feature selection.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- Feasibility and Acceptability of a Psychological Intervention for Internalized Health-Related Stigma Among Adults With Chronic Health Conditions: Preliminary Investigation
2025 · JMIR Formative Research
Tests if a psychological intervention helps adults with chronic health conditions.
- Protocol for an observational cohort study integrating real-world data and microsimulation to assess imaging surveillance strategies in stage I–IIIA NSCLC patients in OneFlorida+
2025 · BMJ Open
Plans a study to test if imaging surveillance improves survival in lung cancer patients.
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- A regularized continuous-time hidden Markov model for identifying latent state transition patterns of poly-tobacco use
2025 · Biometrics
Uses a new model to study tobacco use patterns in a large study.
- PolyGR-containing aggregates link with pathology and clinical features of Alzheimer’s disease
2025 · Acta Neuropathologica
Finds polyGR+ aggregates in Alzheimer's disease brains but not in controls.
- Maternal Myo‐Inositol Intake and Congenital Heart Defects in Offspring: A Population‐Based Case–Control Study
2025 · BJOG An International Journal of Obstetrics & Gynaecology
Investigates if maternal myo-inositol intake affects congenital heart defects in offspring.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Xiangyang Lou hasn't set up this page yet. Their UF Scholars profile lists their publications.