2.7k citations · 2.7k across the 6 of their papers we have counts for
6 papers
Challenges and perspectives in computational deconvolution of genomics data
Lana X. Garmire, Yijun Li, Qianhui Huang +6
Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient ap…
SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways
Waradon Sungnak, Ni Huang, Christophe Bécavin +2
The SARS-CoV-2 coronavirus, the etiologic agent responsible for COVID-19 coronavirus disease, is a global threat. To better understand viral tropism, we assessed the RNA expression…
Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension
Molei Liu, Jiehuan Sun, Jose D. Herazo-Maya +2
Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to deal with different ty…
Distance-Correlation based Gene Set Analysis in Longitudinal Studies
Jiehuan Sun, Jose D. Herazo-Maya, Xiu Huang +2
Longitudinal gene expression profiles of patients are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene…
A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data
Jiehuan Sun, Jose D. Herazo-Maya, Naftali Kaminski +2
Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expressio…
LRSA: A new computational method for analyzing time course microarray data
Wei Wu, Nilesh B. Dave, Naftali Kaminski
Motivation: Time course data obtained from biological samples subject to specific treatments can be very useful for revealing complex and novel biological phenomena. Although an in…