Information Integration via Bayesian and Neural Network Models With Applications to Biology
Citationhu, zhirui. 2019. Information Integration via Bayesian and Neural Network Models With Applications to Biology. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractThis thesis is comprised of three parts: 1) we proposed a new method to model convergent rate changes of genomic elements on phylogenetic trees and detect the association between the rate shifts and the convergent phenotypes; 2) we introduced a novel approach for nonparmateric regression using neural networks when the variables have measurement errors; 3) we developed a new method for imputation and clustering for single cell RNA sequencing data.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42013062
- FAS Theses and Dissertations