Revolutionizing Diet and Health with CNN's and the Microbiome

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=G9BBaybgfrU


Duration: 3:41
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5-min ML Paper Challenge
Presenter: https://www.linkedin.com/in/anupra-chandran-800b9814b/

Using convolutional neural networks to explore the microbiome
https://www.researchgate.net/publication/320113890_Using_convolutional_neural_networks_to_explore_the_microbiome

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.



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Tags:
deep learning
machine learning
diet
health
bacteria
cancer
diabetes
obesity
AI
data
gut
microbiome
ConvNet
CNN
Convolutional Networks