Using deep learning methods to predict the VRC01 neutralization sensitivity by HIV-1 gp160 sequence features Öffentlichkeit

Abstract

Introduction: The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. Our object is to applied Deep learning (DL) methods to see whether or not DL models can help improve accuracy on predicting sensitivity of neutralization of 611 HIV-1 Env pseudoviruses by VRC01.

Methods: We tried three different kinds of Deep Neural Network structures (FCNN, 1D-CNN, 1D-CNN+BiLSTM) to do the prediction and implemented a 5-fold cross-validation method to verify the performance of the model. We chose best model of each three neural network structures to do the prediction on our test set. We selected accuracy (Accuracy, Acc), precision (P), recall (R), F1 score (F1) and average area under the receiver operating characteristics (ROC) curve as evaluation indicators.

Results: The three mean AUCs (area under curve) for ROC curves are , , respectively. The prediction accuracies are 0.85, 0.83, 0.85 respectively.

Conclusion: For this small sample size task, our three Deep Learning models did not perform as well as the random forest model.

Key words: Human Immunodeficiency Virus (HIV), Antibody Mediated Prevention (AMP), Deep Learning, Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)

Table of Contents

None

About this Master's Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Biostatistics
Subfield / Discipline	Public Health Informatics - MSPH
Degree	M.S.
Submission	Master's Thesis
Language	English
Research Field	Biology, Biostatistics
Stichwort	Deep Learning Recurrent Neural Network (RNN) Convolutional Neural Network (CNN) Human Immunodeficiency Virus (HIV) Long Short-Term Memory (LSTM) Deep Neural Network (DNN) Antibody Mediated Prevention (AMP)
Committee Chair / Thesis Advisor	Benkeser, David, Emory University
Committee Members	Wu, Hao, Emory University

Zuletzt geändert

Primary PDF

Thumbnail	Title	Date Uploaded	Actions
	Using deep learning methods to predict the VRC01 neutralization sensitivity by HIV-1 gp160 sequence features ()	2020-04-15 12:38:07 -0400	Press to Select an action Download

Supplemental Files

Thumbnail	Title	Date Uploaded	Actions