MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction.

Abstract

Motivation: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exists some plasmids that can transfer, replicate or persist in multiple organisms. Identifying the relatively complete host range of these plasmids provides insights into how plasmids promote bacterial evolution. To achieve this, we can apply multi-label learning models for plasmid host range prediction. However, there are no databases providing the detailed and complete host labels of these broad-host-range (BHR) plasmids. Without adequate well-annotated training samples, learning models can fail to extract discriminative feature representations for plasmid host prediction.

Results: To address this problem, we propose a self-correction multi-label learning model called MOSTPLAS. We design a pseudo label learning algorithm and a self-correction asymmetric loss to facilitate the training of multi-label learning model with samples containing some unknown missing labels. We conducted a series of experiments on NCBI RefSeq plasmid database, PLSDB 2025 database, plasmids with experimentally determined host labels, Hi-C dataset and DoriC dataset. The benchmark results against other plasmid host range prediction tools demonstrated that MOSTPLAS recognized more host labels while keeping a high precision.

Availability and implementation: MOSTPLAS is implemented with Python, which can be downloaded at https://github.com/wzou96/MOSTPLAS. All relevant data we used in the experiments can be found at 10.5281/zenodo.14708999.

Contact and supplementary information: Please contact: yannisun@cityu.edu.hk. Supplementary data are available at Bioinformatics online.