Metagenomic Taxonomic Classification Using Extreme Learning Machines

By Z. Rasheed, Huzefa Rangwala. 2012

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Neural networks can be slow and therefore have had limited use for reference-based taxonomic classification, with TAC-ELM [249] being the only neural network-based algorithm to taxonomically classify massive amounts of metagenomic data.”
Trends in extreme learning machines: A review (2015, unknown): “…al. (2012), Karpagachelvi et al. (2012), Kaya and Uyar (2013), Kim et al. (2009), Li, Ouyang, et al. (2012), Osman et al. (2012), Pan et al. (2012), Rasheed and Rangwala (2012), Saraswathi et al. (2012), Savojardo et al. (2011), Shi et al. (2013), Yuan et al. (2011b, 2012), You et al. (2013),…”
Machine learning for metagenomics: methods and tools (2015, methodology): “…Classifier (Chaudhary et al., 2015) uses Random Forests for the taxonomic classification of short 16S rRNA and the Greengenes taxonomy for assignment, while TAC-ELM (Rasheed and Rangwala, 2012) is a composition-based method (oligonucleotides and GC content) that uses a neural network-based model.”