TY - JOUR AU - Dhungel, Eliza AU - Mreyoud, Yassin AU - Gwak, Ho-Jin AU - Rajeh, Ahmad AU - Rho, Mina AU - Ahn, Tae-Hyuk PY - 2021 DA - 2021/01/18 TI - MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning JO - BMC Bioinformatics SP - 25 VL - 22 IS - 1 AB - Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-020-03933-4 DO - 10.1186/s12859-020-03933-4 ID - Dhungel2021 ER -