A multi-task CNN learning model for taxonomic assignment of human viruses.

ABSTRACT:

Background

Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample.

Results

In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN mo

SUBMITTER: Ma H 

PROVIDER: S-EPMC8170063 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature