Predicting Influenza A Viral Host Using PSSM and Word Embeddings

TL;DR

This paper presents machine learning models utilizing PSSM and word embeddings to accurately predict the host species of Influenza A viruses, aiding early detection and containment efforts.

Contribution

It introduces a novel combination of PSSM features and word embeddings for viral host prediction, achieving high accuracy in distinguishing host origins.

Findings

PSSM-based model achieves MCC ~95% and F1 ~96%.

Word embedding model achieves MCC ~96% and F1 ~97%.

Models demonstrate high accuracy in viral host inference.

Abstract

The rapid mutation of the influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1 around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.