  Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in
cattle with multiple etiologies, including bacterial and viral. It is estimated
that mortality, morbidity, therapy, and quarantine resulting from BRDC account
for significant losses in the cattle industry. Early detection and management
of BRDC are crucial in mitigating economic losses. Current animal disease
diagnostics is based on traditional tests such as bacterial culture, serolog,
and Polymerase Chain Reaction (PCR) tests. Even though these tests are
validated for several diseases, their main challenge is their limited ability
to detect the presence of multiple pathogens simultaneously. Advancements of
data analytics and machine learning and applications over metagenome sequencing
are setting trends on several applications. In this work, we demonstrate a
machine learning approach to identify pathogen signatures present in bovine
metagenome sequences using k-mer-based network embedding followed by a deep
learning-based classification task. With experiments conducted on two different
simulated datasets, we show that networks-based machine learning approaches can
detect pathogen signature with up to 89.7% accuracy. We will make the data
available publicly upon request to tackle this important problem in a difficult
domain.
