Qunfeng Dong : A Computational Biologist’s Bayesian Journey

Posted by Jie Yang , part of the Statistics and Data Science Seminar.

At

Sept. 7, 2022, 4 p.m.

In

636 SEO

Abstract

Many biomedical researchers including myself lack the formal training in Bayesian statistics, yet we have found the beauty and magic in it. In this talk, I will present three projects: (1) Bayesian modeling to estimate hospitalization risk for COVID-19 patients with comorbidities, (2) a microbiome taxonomic classification method based on Bayes theorem and bootstrapping, and (3) predicting clinical outcomes of metastatic melanoma patients based on the commensal microbiome using a Bayes’ classifier. I will also highlight the limitations of our methods, so that hopefully hardcore mathematicians/statisticians can come up with better solutions. References: 1. Xiang Gao and Qunfeng Dong (2020) A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus. Journal of the American Medical Informatics Association, 28 Sept 2020, ocaa246, doi:10.1093/jamia/ocaa246 2. Xiang Gao, Huaiying Lin, Qunfeng Dong (2017); A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions, mSphere, Volume: 2, Issue: 6. 3. Xiang Gao, Huaiying Lin, Kashi Revanna, Qunfeng Dong (2017) A Bayesian Taxonomic Classification Method for 16S rRNA Gene Sequences with Improved Species-level Accuracy. BMC Bioinformatics 2017 May 10;18(1):247.