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Found 5 papers, 0 papers with code
TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification
In this study, we propose addressing this problem through zero-shot learning using TEPI, Taxonomy-aware Embedding and Pseudo-Imaging.
Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning
The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis.
Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis
There is an increased need for learning robust representations from metagenome reads since pathogens within a family can have highly similar genome structures (some more than 90%) and hence enable the segmentation and identification of novel pathogen sequences with limited labeled data.
MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis
However, there are significant challenges in developing such an approach, the chief among which is to learn self-supervised representations that can help detect novel pathogen signatures with very low amounts of labeled data.
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks
Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
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