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Poster Session II

Experimental Comparison with Varying Lengths of K-mer and Stride for Microbial Taxonomy

초록

영어

In regard to recent advancements in metagenomic sequencing, it is now possible to sequence large numbers of microbial genomes with ease. Taxonomic classification of metagenomic data remains a crucial task as it provides useful information in finding relationships between other microbial species in a given area or possible infectious diseases. Over the past decade, deep learning has proven to be a powerful tool in classifying multiple objects. By combining both studies it is possible to gain taxonomic classification of metagenomic data with proficiency.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. EXPERIMENTS
A. Dataset
B. Models
C. Experimental results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자정보

  • Sung-Yoon Ahn Pattern Recognition and Machine Learning Lab Gachon University
  • Ji-Soo Tak Pattern Recognition and Machine Learning Lab Gachon University
  • Sang-Woong Lee Pattern Recognition and Machine Learning Lab Gachon University

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