Machine Learning Approaches for Metagenomics
Abstract
Microbes exists everywhere. Current generation of genomic technologies have allowed researchers to determine the collective DNA sequence of all microorganisms co-existing together. In this paper, we present some of the challenges related to the analysis of data obtained from the community genomics experiment (commonly referred by metagenomics), advocate the need of machine learning techniques and highlight our contributions related to development of supervised and unsupervised techniques for solving this complex, real world problem.
Cite
Text
Rangwala et al. "Machine Learning Approaches for Metagenomics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_47Markdown
[Rangwala et al. "Machine Learning Approaches for Metagenomics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/rangwala2014ecmlpkdd-machine/) doi:10.1007/978-3-662-44845-8_47BibTeX
@inproceedings{rangwala2014ecmlpkdd-machine,
title = {{Machine Learning Approaches for Metagenomics}},
author = {Rangwala, Huzefa and Charuvaka, Anveshi and Rasheed, Zeehasham},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {512-515},
doi = {10.1007/978-3-662-44845-8_47},
url = {https://mlanthology.org/ecmlpkdd/2014/rangwala2014ecmlpkdd-machine/}
}