Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.

ABSTRACT: Accurate and precise identification of adeno-associated virus (AAV) vectors play an important role in dose-dependent gene therapy. Although solid-state nanopore techniques can potentially be used to characterize AAV vectors by capturing ionic current, the existing data analysis techniques fall short of identifying them from their ionic current profiles. Recently introduced machine learning methods such as deep convolutional neural network (CNN), developed for image identification tasks, can be applied for such classification. However, with smaller data set for the problem in hand, it is not possible to train a deep neural network from scratch for accurate classification of AAV vectors. To circumvent this, we applied a pre-trained deep CNN (GoogleNet) model to capture the basic features fro

SUBMITTER: Khan AI 

PROVIDER: S-EPMC9897492 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature