Active learning for efficient analysis of high-throughput nanopore data.

ABSTRACT:

Motivation

As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technolog

SUBMITTER: Guan X 

PROVIDER: S-EPMC9825740 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature