“EviMass”: A Literature Evidence-Based Miner for Human Microbial Associations

Abstract

The importance of understanding microbe–microbe as well as microbe–disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations. In this communication, we present a tool called “EviMass” (Evidence based mining of human Microbial Associations), which can assist biologists to validate their predicted hypotheses from new microbiome studies. Users can interactively query the processed back-end database for microbe–microbe and disease–microbe associations. The EviMass tool can also be used to upload microbial association networks generated from a human “disease–control” microbiome study and validate the associations from biomedical literature. Additionally, a list of differentially abundant microbes for the corresponding disease can be queried in the tool for reported evidences. The results are presented as graphical plots, tabulated summary, and other evidence statistics. EviMass is a comprehensive platform and is expected to enable microbiome researchers not only in mining microbial associations, but also enriching a new research hypothesis. The tool is available free for academic use at .

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

“…Further, we tried to depict issues that we think must be considered before using an ARM approach for specifical biological traits. As there is an interest in research to exploit data mining techniques, citing for example the works of Srivastava et al, 2019 or Zakrzewski et al, 2017 , we also think that suiting ARM for microbiome analysis will be a great resource in the future. Considering the huge amount of data available and produced with the advent of High-Throughput DNA Sequencing (HTS) technologies, an increasing selection of large-scale data science strategies seems to have enormous potential in resolving challenges in microbiome pattern exploration ( Jordan and Mitchell, 2015 ; Kypides et al, 2016 ).…”

Progress on network modeling and analysis of gut microecology: a review

“…In the realm of network topology and modular analysis, Baldassano discovered distinctions in microbiota structure between individuals with IBD and healthy counterparts ( 112 ). Meanwhile, Srivastava et al ( 113 ) introduced EviMass to validate hypotheses arising from microbiome research, facilitating interactive querying of microbial-microbial and disease-microbial associations within a processed backend database. For example, Xiao et al ( 103 ) introduced NetMoss, a method that evaluates shifts within microbial network modules to pinpoint robust biomarkers associated with multiple diseases.…”