Outreach

@misc{nokey,

title = {Computational prediction of virus-host interactions by pre-trained transformer and Multiple Instance Learning},

author = {Dan Liu},

url = {https://cbc.dcs.gla.ac.uk/},

year  = {2022},

date = {2022-09-01},

urldate = {2022-09-01},

abstract = {Viruses exist in the environment extensively and live in different hosts including plants, animals, eukaryotes, and bacteria and generate offspring depending on the replication mechanisms of the hosts. Detecting virus-host interactions is essential for us to understand interaction mechanisms between virus and host and explore clues regarding diseases caused by viruses. The virus mimics the host's patterns for escaping the immune response, so viral contain predictive signals for host prediction. Here, we proposed a computational strategy to predict hosts for viruses, which extracted representative vectors of viral protein sequences through a pre-trained transformer language model and then passed protein features to attention-based Multiple instance learning for host prediction and obtaining each weighted protein for viruses. Protein weight is helpful for understanding essential proteins contributing to virus-host interactions, and it is explainable for prediction signals of host prediction at the species level. Our results demonstrated that transformer embedding is potential for virus-host interaction prediction and is able to explain essential virus proteins leading to host infection.

},

howpublished = {Oral presentation at Computational Biology Conference 2022, Glasgow, UK},

keywords = {Project 10, WP 1.2 Host prediction},

pubstate = {published},

tppubtype = {presentation}

}

@misc{nokey,

title = {Replidec - Use naive bayes classifier to identify virus lifecycle from metagenomics data},

author = {Xue Peng},

year  = {2022},

date = {2022-08-01},

howpublished = {Poster at VIRO retreat 2022, Munich, Germany},

keywords = {Project 05, WP 1.2 Host prediction},

pubstate = {published},

tppubtype = {presentation}

}