Developing a new Phylogeny-driven Random Forest Model for Functional Metagenomics

Jyotsna talreja Wassan, Haiying Wang, Huiru Zheng

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
116 Downloads (Pure)


Metagenomics is an unobtrusive science linking microbial genes to biological functions or environmental states. Classifying microbial genes into their functional repertoire is an important task in the downstream analysis of Metagenomic studies. The task involves Machine Learning (ML) based supervised methods to achieve good classification performance. Random Forest (RF) has been applied rigorously to microbial gene abundance profiles, mapping them to functional phenotypes. The current research targets tuning RF by the evolutionary ancestry of microbial phylogeny, developing a Phylogeny-RF model for functional classification of metagenomes. This method facilitates capturing the effects of phylogenetic relatedness in an ML classifier itself rather than just applying a supervised classifier over the raw abundances of microbial genes. The idea is rooted in the fact that closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together, or one of these could be dropped from the analysis, to improve the ML process. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware methods of MetaPhyl and PhILR, using three real-world 16S rRNA metagenomic datasets. It has been observed that the proposed method not only achieved significantly better performance than the traditional RF model but also performed better than the other phylogeny-driven benchmarks ( p < 0.05 ). For example, Phylogeny-RF attained a highest AUC of 0.949 and Kappa of 0.891 over soil microbiomes in comparison to other benchmarks.
Original languageEnglish
Pages (from-to)763-770
Number of pages8
JournalIEEE Transactions on Nanobioscience
Issue number4
Early online date6 Jun 2023
Publication statusPublished (in print/issue) - 31 Oct 2023

Bibliographical note

Publisher Copyright:
© 2002-2011 IEEE.


  • Metagenomics
  • Phylogeny
  • Random Forest
  • Operational Taxonomic Units (OTUs)
  • Classification
  • Clustering
  • Classification tree analysis
  • Clustering algorithms
  • Biological system modeling
  • Microorganisms
  • Radio frequency
  • Random forests


Dive into the research topics of 'Developing a new Phylogeny-driven Random Forest Model for Functional Metagenomics'. Together they form a unique fingerprint.

Cite this