An Integrative Framework for Functional Analysis of Cattle Rumen Microbiomes

Wassan, Jyotsna Talreja; Zheng, Huiru; Browne, Fiona; Bowen, J M; Walsh, Paul; Roehe, R.; Dewhurst, Richard J.; Palu, Cintia; Kelly, Brian; Wang, Haiying

doi:10.1109/bibm.2018.8621104

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The architecture was designed based on best practices of big data systems, to ensure scalability. Another goal was to use new and innovative ML models and visualizations to help researchers in understanding the collected data [95,96].…”

Section: Metaplat and Successorsmentioning

confidence: 99%

Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

show abstract

Section: Metaplat and Successorsmentioning

confidence: 99%

Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A current challenge in functional metagenomics is to model the phylogenetic effects with high-dimensional abundance profiles of microbial genes in ML models. Very limited research in this area has been conducted [15]- [19]. Some regularization methods have been recommended as classifiers in literature [20],Error!…”

Section: Introductionmentioning

confidence: 99%

Developing a New Phylogeny-Driven Random Forest Model for Functional Metagenomics

Wassan

Wang

Zheng

2023

IEEE Trans.on Nanobioscience

Self Cite

View full text Add to dashboard Cite

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. 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 stateof-the-art classification methods including RF and the phylogenyaware method of MetaPhyl, using two real-world 16S rRNA metagenomic datasets. The proposed method was observed to perform better than the other phylogeny-driven benchmarks. For example, Phylogeny-RF attained a high AUC of 0.949 over soil microbiomes in comparison to other benchmarks.

show abstract