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Risk Prediction with Longitudinal Gene Expression Data Using Statistical and Machine Learning Method

Description:

With the advancement of high-throughput sequencing technology, it has become much easier to extract gene expression data and to discover gene-disease associations more efficiently. Longitudinal gene expression data offer more insight into expression patterns for distinct patient groups compared to cross-sectional data. For instance, patients diagnosed with subclinical acute rejections...

Keyword:

mixed effect estimation, Empirical Bayes, two-stage modeling, and dynamic predictions

Subject:

Statistics

Creator:

Lyu, Jiahui

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_982900 and http://dissertations.umi.com/northwestern:16506