Understanding the host-specificity of different families of viruses sheds
light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic
pathogens in humans. It enables epidemiologists, medical professionals, and
policymakers to curb existing epidemics and prevent future ones promptly. In
the family Coronaviridae (of which SARS-CoV-2 is a member), it is well-known
that the spike protein is the point of contact between the virus and the host
cell membrane. On the other hand, the two traditional mammalian orders,
Carnivora (carnivores) and Chiroptera (bats) are recognized to be responsible
for maintaining and spreading the Rabies Lyssavirus (RABV). We propose
Virus2Vec, a feature-vector representation for viral (nucleotide or amino acid)
sequences that enable vector-space-based machine learning models to identify
viral hosts. Virus2Vec generates numerical feature vectors for unaligned
sequences, allowing us to forego the computationally expensive sequence
alignment step from the pipeline. Virus2Vec leverages the power of both the
\emph{minimizer} and position weight matrix (PWM) to generate compact feature
vectors. Using several classifiers, we empirically evaluate Virus2Vec on
real-world spike sequences of Coronaviridae and rabies virus sequence data to
predict the host (identifying the reservoirs of infection). Our results
demonstrate that Virus2Vec outperforms the predictive accuracies of baseline
and state-of-the-art methods.

通过 Virus2Vec 生成的数字特征向量，可以检测病毒感染的宿主，实验结果证明了 Virus2Vec 优于其他方法。