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. © 2014 Springer-Verlag.
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CITATION STYLE
Rangwala, H., Charuvaka, A., & Rasheed, Z. (2014). Machine learning approaches for metagenomics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8726 LNAI, pp. 512–515). Springer Verlag. https://doi.org/10.1007/978-3-662-44845-8_47
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