Immunologic Correlates Analysis of RhCMV/SIV Vaccine Efficacy: Applying Machine Learning Techniques to Model Vaccine Elicited T Cell Responses

Text Registration

Immunologic Correlates Analysis of RhCMV/SIV Vaccine Efficacy: Applying Machine Learning Techniques to Model Vaccine Elicited T Cell Responses

Status

Published

on 25 Feb 2019

Year of Creation

2018

Wenjun Song

Registration Number

TX0008679948

on 25 Feb 2019

The U.S. Copyright record (Registration Number: TX0008679948) dated 25 Feb 2019, pertains to an electronic file (eService) titled "Immunologic Correlates Analysis of RhCMV/SIV Vaccine Efficacy: Applying Machine Learning Techniques to Model Vaccine Elicited T Cell Responses" created in 2018. The copyright holder is Wenjun Song, known for their creative contributions in text registration. For any inquiries concerning this copyrighted material, kindly reach out to Wenjun Song.

Wenjun Song

Application Details

Registration Number

TX0008679948

Registration Date

2/25/2019

Year of Creation

2018

Agency Marc Code

DLC-CO

Record Status

New

Physical Description

Electronic file (eService)

First Publication Nation

United States

Personal Authors

Wenjun Song

Notes

Rights Note: Mark Dill, ProQuest-CSA, LLC, 789 E. Eisenhower Parkway, Ann Arbor, MI, 48108-3218, United States, (800) 521-0600, disspub@proquest.com

Statements

Application Title Statement: Immunologic Correlates Analysis of RhCMV/SIV Vaccine Efficacy: Applying Machine Learning Techniques to Model Vaccine Elicited T Cell Responses

Author Statement: Wenjun Song Citizenship: not known Authorship: text

Notes

Rights Note: Mark Dill, ProQuest-CSA, LLC, 789 E. Eisenhower Parkway, Ann Arbor, MI, 48108-3218, United States, (800) 521-0600, disspub@proquest.com

Statements

Application Title Statement: Immunologic Correlates Analysis of RhCMV/SIV Vaccine Efficacy: Applying Machine Learning Techniques to Model Vaccine Elicited T Cell Responses

Author Statement: Wenjun Song Citizenship: not known Authorship: text

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