Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks

• URL: http://arxiv.org/abs/2007.12791v1
• Date: Fri, 24 Jul 2020 22:30:18 GMT
• Title: Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks
• Authors: Sai Narayanan, Akhilesh Ramachandran, Sathyanarayanan N. Aakur, Arunkumar Bagavathi
• Abstract summary: Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral. Current animal disease diagnostics is based on traditional tests such as bacterial culture, serolog, and Polymerase Chain Reaction (PCR) tests. We show that networks-based machine learning approaches can detect pathogen signature with up to 89.7% accuracy.
• Score: 4.339839287869652
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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